{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow import keras\n",
    "import tensorflow as tf\n",
    "import numpy as np\n",
    "import json\n",
    "import matplotlib.pyplot as plt\n",
    "import os\n",
    "import sys"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "if not os.path.isdir(\"data\"):\n",
    "    print(\"not have train data exit!\")\n",
    "    sys.exit()\n",
    "\n",
    "train_data = json.load(open('./data/train.txt', 'r'))\n",
    "val_data = json.load(open('./data/val.txt', 'r'))\n",
    "\n",
    "test_data = json.load(open('./data/test.txt', 'r'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 绘制数据集的图表\n",
    "def plot_data(data):\n",
    "    cos_x = [item['x'] for item in data if item['curve'] == 'cos']\n",
    "    cos_y = [item['y'] for item in data if item['curve'] == 'cos']\n",
    "    \n",
    "    sin_x = [item['x'] for item in data if item['curve'] == 'sin']\n",
    "    sin_y = [item['y'] for item in data if item['curve'] == 'sin']\n",
    "    \n",
    "    plt.figure(figsize=(12, 6))\n",
    "    plt.plot(cos_x, cos_y, label='cos', color='blue', marker='o', linestyle='')\n",
    "    plt.plot(sin_x, sin_y, label='sin', color='red', marker='x', linestyle='')\n",
    "    plt.title('Cosine and Sine Curves')\n",
    "    plt.xlabel('x')\n",
    "    plt.ylabel('y')\n",
    "    plt.legend()\n",
    "    plt.grid(True)\n",
    "    plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 绘制训练集和验证集的图表\n",
    "plot_data(train_data)\n",
    "plot_data(val_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "inputs = tf.keras.Input(shape=(2,))\n",
    "\n",
    "w1 = keras.layers.Dense(16, activation='relu', input_shape=(1,), name=\"relu\")(inputs)\n",
    "w2 = keras.layers.Dense(16, activation='relu', name=\"relu2\")(w1)\n",
    "outputs = keras.layers.Dense(1, activation = 'sigmoid', name=\"output\")(w2)\n",
    "\n",
    "model = keras.models.Model(inputs=inputs, outputs=outputs)# type: ignore\n",
    "# 编译模型\n",
    "model.compile(optimizer='adam', loss='mse', metrics=['mae'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input_1 (InputLayer)         [(None, 2)]               0         \n",
      "_________________________________________________________________\n",
      "relu (Dense)                 (None, 16)                48        \n",
      "_________________________________________________________________\n",
      "relu2 (Dense)                (None, 16)                272       \n",
      "_________________________________________________________________\n",
      "output (Dense)               (None, 1)                 17        \n",
      "=================================================================\n",
      "Total params: 337\n",
      "Trainable params: 337\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "def print_logo(train_x, train_y, val_x, val_y, test_x, test_y):\n",
    "    print('-'*40)\n",
    "    print(\"train data\")\n",
    "    print('-'*40)\n",
    "    print(train_x)\n",
    "    print(train_y)\n",
    "    print()\n",
    "    print('-'*40)\n",
    "    print(\"val data\")\n",
    "    print('-'*40)\n",
    "    print(val_x)\n",
    "    print(val_y)\n",
    "    print()\n",
    "    print('-'*40)\n",
    "    print(\"val data\")\n",
    "    print('-'*40)\n",
    "    print(test_x)\n",
    "    print(test_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----------------------------------------\n",
      "train data\n",
      "----------------------------------------\n",
      "[[ 4.40892182 -1.07751226]\n",
      " [ 3.88689542 -0.83228362]\n",
      " [ 2.9686321  -1.06603003]\n",
      " ...\n",
      " [ 0.52831588  0.92016411]\n",
      " [ 5.40894831  0.69064415]\n",
      " [ 2.25163197 -0.64006764]]\n",
      "[0 0 1 ... 1 1 1]\n",
      "\n",
      "----------------------------------------\n",
      "val data\n",
      "----------------------------------------\n",
      "[[ 5.18252722e+00  3.81851852e-01]\n",
      " [ 4.30829023e+00 -3.47507834e-01]\n",
      " [ 1.57236870e+00 -1.09378979e-01]\n",
      " [ 2.13213195e+00  1.01384199e+00]\n",
      " [ 1.16984231e+00  1.06292927e+00]\n",
      " [ 9.68579116e-01  9.17899370e-01]\n",
      " [ 5.55360623e+00  7.32625842e-01]\n",
      " [ 1.86797401e+00 -9.49127376e-02]\n",
      " [ 5.71084310e+00 -4.00766879e-01]\n",
      " [ 1.50947395e-01  9.94668484e-01]\n",
      " [ 1.50947395e-01  2.26037763e-02]\n",
      " [ 5.94984314e+00  7.54268348e-01]\n",
      " [ 5.00642193e+00  3.12742621e-01]\n",
      " [ 1.06921071e-01 -2.30294410e-02]\n",
      " [ 4.68565871e+00 -9.71928716e-01]\n",
      " [ 4.62276396e+00 -5.85398115e-02]\n",
      " [ 3.28939531e+00 -8.08862820e-02]\n",
      " [ 1.06292124e+00  8.83190393e-01]\n",
      " [ 3.03152684e+00  1.28838271e-01]\n",
      " [ 4.22652705e+00 -6.89723194e-01]\n",
      " [ 3.15731634e+00 -3.33198607e-02]\n",
      " [ 6.18255371e+00  1.05114114e+00]\n",
      " [ 1.69815819e+00 -6.46666810e-02]\n",
      " [ 1.01889491e+00  1.00431824e+00]\n",
      " [ 5.03157983e-02  7.31435269e-02]\n",
      " [ 6.98131701e-01  7.40675211e-01]\n",
      " [ 1.91200033e+00  1.06414342e+00]\n",
      " [ 8.05052772e-01  5.49515486e-01]\n",
      " [ 4.44036920e+00 -1.03077614e+00]\n",
      " [ 5.74857995e+00  8.35492373e-01]\n",
      " [ 3.83029014e+00 -8.48739386e-01]\n",
      " [ 5.79260627e+00 -5.47545433e-01]\n",
      " [ 6.35236953e-01  9.36234653e-01]\n",
      " [ 2.43402674e+00 -7.92696178e-01]\n",
      " [ 4.79257978e+00 -1.02664280e+00]\n",
      " [ 3.04410579e+00  3.80161442e-02]\n",
      " [ 1.96231613e+00 -3.72774541e-01]\n",
      " [ 1.22015811e+00  2.70807803e-01]\n",
      " [ 4.62905344e+00 -1.40493825e-01]\n",
      " [ 5.86807997e+00 -3.36214215e-01]\n",
      " [ 2.76736890e-01  9.88374114e-01]\n",
      " [ 4.72968504e+00  6.25963211e-02]\n",
      " [ 4.55986922e+00 -7.79905096e-02]\n",
      " [ 1.09436861e+00  4.10653144e-01]\n",
      " [ 5.33976409e+00  5.21345317e-01]\n",
      " [ 1.54721080e+00 -9.81717184e-02]\n",
      " [ 2.23276355e+00 -4.66154456e-01]\n",
      " [ 4.44665867e+00 -2.52764612e-01]\n",
      " [ 7.42158024e-01  7.72761464e-01]\n",
      " [ 3.08813212e+00  1.07460693e-01]\n",
      " [ 4.24539548e+00 -5.04494727e-01]\n",
      " [ 1.65413187e+00  8.69764507e-01]\n",
      " [ 8.99394894e-01  6.14208221e-01]\n",
      " [ 2.13842143e+00  8.63528490e-01]\n",
      " [ 6.28318531e+00 -4.63034771e-03]\n",
      " [ 3.65418485e+00 -9.37955797e-01]\n",
      " [ 3.39002691e+00 -7.98372328e-01]\n",
      " [ 3.63531642e+00 -5.83471656e-01]\n",
      " [ 5.78631680e-01  5.36351442e-01]\n",
      " [ 3.52839535e+00 -5.58741689e-01]\n",
      " [ 4.57873764e+00 -2.18374521e-01]\n",
      " [ 2.15728985e+00 -5.32567024e-01]\n",
      " [ 4.10073756e+00 -7.94872344e-01]\n",
      " [ 2.66673731e+00  5.16058028e-01]\n",
      " [ 2.16986880e+00  8.92987013e-01]\n",
      " [ 2.54723729e+00 -5.61631918e-01]\n",
      " [ 4.49697447e+00 -3.70709956e-01]\n",
      " [ 2.51578991e-02  4.51721549e-02]\n",
      " [ 1.39626340e+00  4.07655090e-01]\n",
      " [ 2.08181615e+00 -4.67293084e-01]\n",
      " [ 3.58500063e+00 -8.34120274e-01]\n",
      " [ 2.52207939e+00 -7.88017213e-01]\n",
      " [ 1.89313191e+00  1.11951995e+00]\n",
      " [ 4.94981665e+00  1.78685114e-01]\n",
      " [ 3.30197426e+00 -1.03151023e+00]\n",
      " [ 1.71073714e+00  1.03904057e+00]\n",
      " [ 4.66050081e+00 -1.06058776e+00]\n",
      " [ 1.12581599e+00  9.70662236e-01]\n",
      " [ 1.43400025e+00  2.46263474e-01]\n",
      " [ 5.37121146e+00  5.19436181e-01]\n",
      " [ 1.93086876e+00 -3.29969585e-01]\n",
      " [ 2.01263193e-01  2.21031290e-02]\n",
      " [ 3.42147428e+00 -2.98200071e-01]\n",
      " [ 4.01897439e+00 -7.17263818e-01]\n",
      " [ 1.47802657e+00  1.55394346e-01]\n",
      " [ 6.01273789e+00 -2.99918175e-01]\n",
      " [ 5.79260627e+00  8.54460597e-01]\n",
      " [ 3.81771119e+00 -3.49111706e-01]\n",
      " [ 4.90579033e-01  9.24201310e-01]\n",
      " [ 8.49079096e-01  6.10729933e-01]\n",
      " [ 5.50329043e+00  8.76884103e-01]\n",
      " [ 5.29573777e+00  5.61195791e-01]\n",
      " [ 5.78631680e+00 -3.43349189e-01]\n",
      " [ 7.61026449e-01  6.14499748e-01]\n",
      " [ 3.16989529e+00  4.11811471e-03]\n",
      " [ 2.42773727e+00 -9.81870532e-01]\n",
      " [ 1.11323704e+00  8.55898917e-01]\n",
      " [ 4.45923762e+00 -4.66660410e-01]\n",
      " [ 3.42147428e+00 -8.79343390e-01]\n",
      " [ 4.50955342e+00 -1.01429164e-01]\n",
      " [ 4.59131659e-01  5.07209063e-01]\n",
      " [ 2.76107943e+00 -9.89420056e-01]\n",
      " [ 5.10705352e+00  3.35218191e-01]\n",
      " [ 4.24539548e+00 -6.34651005e-01]\n",
      " [ 5.78002732e+00  8.01162720e-01]\n",
      " [ 2.26421092e+00 -5.79823673e-01]\n",
      " [ 1.74847399e+00 -3.14130247e-01]\n",
      " [ 2.00005298e+00 -4.11675602e-01]\n",
      " [ 5.84921155e-01  6.69439793e-01]\n",
      " [ 1.68557924e+00 -7.36008584e-02]\n",
      " [ 1.00631597e+00  6.72835886e-01]\n",
      " [ 8.55368570e-01  7.64147758e-01]\n",
      " [ 1.52205290e+00 -2.59227809e-02]\n",
      " [ 6.20771161e+00  6.55612210e-03]\n",
      " [ 2.40257937e+00  5.66191494e-01]\n",
      " [ 3.90576384e+00 -7.16723204e-01]\n",
      " [ 3.07555317e+00 -9.49034467e-02]\n",
      " [ 2.72334258e+00 -7.72978783e-01]\n",
      " [ 1.03776334e+00  8.76553059e-01]\n",
      " [ 5.68568520e+00 -6.21117055e-01]\n",
      " [ 3.81142172e+00 -7.39681304e-01]\n",
      " [ 1.77363189e+00 -2.94555187e-01]\n",
      " [ 3.54097430e+00 -6.07511342e-01]\n",
      " [ 4.26426390e+00 -5.25448561e-01]\n",
      " [ 4.39634287e+00 -2.49687344e-01]\n",
      " [ 4.49068499e+00 -9.62798357e-01]\n",
      " [ 4.81144821e+00 -9.97589111e-01]\n",
      " [ 4.14476388e+00 -8.78091097e-01]\n",
      " [ 1.04405281e+00  6.20676935e-01]\n",
      " [ 2.81768470e+00  3.06410879e-01]\n",
      " [ 4.08186913e+00 -7.45170891e-01]\n",
      " [ 1.05663176e+00  5.40079534e-01]\n",
      " [ 5.53473781e-01  5.86633623e-01]\n",
      " [ 4.23281653e+00 -8.54257643e-01]\n",
      " [ 5.08818510e+00  4.80202675e-01]\n",
      " [ 1.63526344e-01  3.17155966e-03]\n",
      " [ 3.09442159e+00  1.18269980e-01]\n",
      " [ 7.79894873e-01  6.51755512e-01]\n",
      " [ 2.95605315e+00 -1.06086969e+00]\n",
      " [ 5.72342205e+00 -5.29967725e-01]\n",
      " [ 4.36489550e+00 -1.00937128e+00]\n",
      " [ 4.48439552e+00 -1.03625000e+00]\n",
      " [ 3.79884277e+00 -7.62969613e-01]\n",
      " [ 2.90573735e+00 -8.85465980e-01]\n",
      " [ 2.80510575e+00  3.73864383e-01]\n",
      " [ 4.33973760e-01  1.05610049e+00]\n",
      " [ 1.45915815e+00  1.12461030e-01]\n",
      " [ 2.40886884e+00 -6.80063248e-01]\n",
      " [ 4.59760607e+00 -1.85894072e-01]\n",
      " [ 2.64157941e-01  4.25728112e-01]\n",
      " [ 1.25789496e-02  1.11209297e+00]\n",
      " [ 1.27676338e+00  1.15824258e+00]\n",
      " [ 4.56615869e+00 -1.09329677e+00]\n",
      " [ 2.07552668e-01  2.55543143e-01]\n",
      " [ 5.13221142e+00 -1.01664829e+00]\n",
      " [ 3.80513224e+00 -7.12284207e-01]\n",
      " [ 1.95602666e+00 -2.29567751e-01]\n",
      " [ 5.16365880e+00  4.66452062e-01]\n",
      " [ 1.06292124e+00  5.36582589e-01]\n",
      " [ 5.44668516e+00  7.67078102e-01]\n",
      " [ 4.28942180e+00 -3.62091899e-01]\n",
      " [ 2.88057945e+00  1.23488478e-01]\n",
      " [ 2.81139523e+00  3.07089835e-01]\n",
      " [ 1.57865817e+00  1.16418016e+00]\n",
      " [ 5.52844833e+00 -7.17631221e-01]\n",
      " [ 2.86171103e+00  3.72204669e-02]\n",
      " [ 1.94344771e+00 -4.60731506e-01]\n",
      " [ 5.72971153e+00 -6.15522325e-01]\n",
      " [ 2.89944787e+00 -1.17047215e+00]\n",
      " [ 1.89942138e+00 -2.82870471e-01]\n",
      " [ 2.57868466e-01  2.33983263e-01]\n",
      " [ 2.69189521e+00  4.86203462e-01]\n",
      " [ 1.70444767e+00  9.74317133e-01]\n",
      " [ 4.08186913e+00 -3.03543061e-01]\n",
      " [ 4.64792186e+00 -1.13653064e+00]\n",
      " [ 4.94352718e+00  3.14119190e-01]\n",
      " [ 4.64163239e+00 -1.70283929e-01]\n",
      " [ 8.86815944e-01  5.89177966e-01]\n",
      " [ 2.02521088e+00  7.69010067e-01]\n",
      " [ 3.24536899e+00  1.50436476e-01]\n",
      " [ 3.33342163e-01  1.13681805e+00]\n",
      " [ 8.17631722e-02  1.05203342e+00]\n",
      " [ 2.27050040e+00  7.77730763e-01]\n",
      " [ 3.56613220e+00 -7.97126234e-01]\n",
      " [ 3.00636895e+00 -9.70257640e-01]\n",
      " [ 4.12589546e+00 -1.04644430e+00]\n",
      " [ 5.00013245e+00 -1.03530586e+00]\n",
      " [ 4.18250073e+00 -4.37493473e-01]\n",
      " [ 4.65421134e+00 -3.57506871e-01]\n",
      " [ 6.25802741e+00  2.02277172e-02]\n",
      " [ 3.06297422e+00 -8.13878417e-01]\n",
      " [ 5.53473781e+00  6.46564722e-01]\n",
      " [ 6.09450106e+00  9.36330974e-01]\n",
      " [ 3.10071107e+00 -9.20972764e-01]\n",
      " [ 2.98750052e+00 -1.03555930e+00]\n",
      " [ 4.40263235e-02  1.05562317e+00]\n",
      " [ 6.21400108e+00 -2.03766048e-01]\n",
      " [ 5.96242209e+00 -3.46405834e-01]\n",
      " [ 1.15097389e+00  9.38282907e-01]\n",
      " [ 3.77368487e+00 -6.92368686e-01]\n",
      " [ 9.43421217e-01  6.07980490e-01]\n",
      " [ 1.76105294e-01  3.64029035e-02]\n",
      " [ 1.47173710e+00  1.24930382e+00]\n",
      " [ 3.23907951e+00 -1.09307623e+00]\n",
      " [ 5.83034312e+00  8.69716406e-01]\n",
      " [ 1.40255288e+00  1.00209725e+00]\n",
      " [ 8.67947520e-01  6.31681263e-01]\n",
      " [ 4.60389554e+00 -2.21308231e-01]\n",
      " [ 6.22658003e+00  8.27803135e-01]\n",
      " [ 2.77365838e+00  5.72067022e-01]\n",
      " [ 1.94973718e-01  1.08374524e+00]\n",
      " [ 1.56607922e+00 -4.55149189e-02]\n",
      " [ 4.35860602e+00 -9.50715244e-01]\n",
      " [ 5.22026407e+00  4.04055923e-01]\n",
      " [ 5.37121146e+00 -7.91149080e-01]\n",
      " [ 3.13844792e+00 -1.05331838e+00]\n",
      " [ 5.42781674e+00 -7.62342989e-01]\n",
      " [ 6.18255371e+00 -2.72508264e-02]\n",
      " [ 4.12589546e+00 -6.78710520e-01]\n",
      " [ 4.13847441e+00 -8.98812115e-01]\n",
      " [ 5.91210630e+00 -2.80847669e-01]\n",
      " [ 4.96868508e-01  8.63223135e-01]\n",
      " [ 6.19513266e+00 -1.65538684e-01]\n",
      " [ 2.98121105e+00  1.47231236e-01]\n",
      " [ 3.83657962e+00 -5.33278883e-01]\n",
      " [ 4.41521130e+00 -9.54216495e-02]\n",
      " [ 2.71076363e+00 -9.81653452e-01]\n",
      " [ 2.81139523e+00 -9.48468029e-01]\n",
      " [ 5.30202724e+00  7.00127602e-01]\n",
      " [ 4.50955342e+00 -1.14234924e+00]\n",
      " [ 2.48434254e+00 -7.49054670e-01]\n",
      " [ 2.02521088e+00 -3.95938665e-01]\n",
      " [ 5.40265884e+00 -7.77934909e-01]\n",
      " [ 1.32078970e-01  2.32026353e-01]\n",
      " [ 3.50323745e+00 -2.40818188e-01]\n",
      " [ 4.47181657e+00 -1.52891487e-01]\n",
      " [ 2.30823724e+00 -7.98294902e-01]\n",
      " [ 2.59126361e+00  5.38169801e-01]\n",
      " [ 5.43410621e+00  8.54688764e-01]\n",
      " [ 4.02526386e+00 -4.49995190e-01]\n",
      " [ 2.28307935e+00  7.13207185e-01]\n",
      " [ 3.98752701e+00 -6.50997519e-01]\n",
      " [ 1.49689500e+00 -2.82125510e-02]\n",
      " [ 1.11323704e+00  3.94429743e-01]\n",
      " [ 8.23921196e-01  7.29606986e-01]\n",
      " [ 4.08815861e-01  8.18901420e-01]\n",
      " [ 3.77368487e-02  1.01087534e+00]\n",
      " [ 1.44657920e-01 -2.37491354e-02]\n",
      " [ 6.11965896e+00 -3.54042470e-01]\n",
      " [ 5.33347462e+00 -7.08372474e-01]\n",
      " [ 2.87428998e+00  4.67751533e-01]\n",
      " [ 4.90579033e+00  9.09821466e-02]\n",
      " [ 3.08184264e+00  8.82085860e-02]\n",
      " [ 2.04407930e+00  8.94476354e-01]\n",
      " [ 5.32089567e+00 -7.28871524e-01]\n",
      " [ 2.50950044e+00  6.07454300e-01]\n",
      " [ 3.59757958e+00 -3.97513956e-01]\n",
      " [ 1.46544762e+00  8.61891687e-01]\n",
      " [ 3.37744796e+00 -2.14268044e-01]\n",
      " [ 3.71707960e+00 -8.02551746e-01]\n",
      " [ 3.14473739e+00  1.76959381e-01]\n",
      " [ 5.67310625e+00 -5.63491583e-01]\n",
      " [ 4.87434296e+00 -1.18609977e+00]\n",
      " [ 2.83026365e-01  1.15599430e+00]\n",
      " [ 5.66052730e-01  8.70948553e-01]\n",
      " [ 5.89952735e+00  8.74518514e-01]\n",
      " [ 7.54736974e-02  9.16772664e-01]\n",
      " [ 1.10065809e+00  8.15020084e-01]\n",
      " [ 4.49068499e+00 -1.23187199e-01]\n",
      " [ 4.74226399e+00  4.34072018e-02]\n",
      " [ 4.16992178e+00 -5.88488281e-01]\n",
      " [ 1.72331609e+00  1.20028627e+00]\n",
      " [ 1.82394769e+00  1.07897317e+00]\n",
      " [ 3.58500063e-01  2.64288187e-01]\n",
      " [ 5.55989571e+00 -7.35031962e-01]\n",
      " [ 3.33342163e-01  2.67169833e-01]\n",
      " [ 1.84910559e+00 -3.35732728e-01]\n",
      " [ 2.72963206e+00 -1.04190195e+00]\n",
      " [ 5.85550102e+00 -4.61476594e-01]\n",
      " [ 4.28942180e+00 -9.59644794e-01]\n",
      " [ 6.10079054e+00  8.92299891e-01]\n",
      " [ 3.23279004e+00 -9.36513901e-01]\n",
      " [ 5.52844833e+00  7.37602413e-01]\n",
      " [ 2.92460577e+00 -1.00198436e+00]\n",
      " [ 1.08178966e+00  8.50139260e-01]\n",
      " [ 3.70450065e+00 -5.75689375e-01]\n",
      " [ 5.05044825e+00  2.89320648e-01]\n",
      " [ 1.33336865e+00  2.59625643e-01]\n",
      " [ 5.47813254e+00  7.08257437e-01]\n",
      " [ 3.94350069e+00 -5.41331351e-01]\n",
      " [ 6.15739581e+00  8.97176087e-01]\n",
      " [ 6.21400108e+00  1.11933923e+00]\n",
      " [ 2.38371094e+00 -7.52860785e-01]\n",
      " [ 2.00634246e+00  8.67825866e-01]\n",
      " [ 3.59129010e+00 -7.44689882e-01]\n",
      " [ 4.46552710e+00 -1.07611239e+00]\n",
      " [ 4.81773768e+00  4.63650972e-02]\n",
      " [ 1.32707918e+00  8.94910812e-01]\n",
      " [ 2.61642151e+00  4.34658289e-01]\n",
      " [ 5.08818510e+00 -9.30139363e-01]]\n",
      "[1 1 1 0 0 0 1 1 0 1 0 1 1 0 0 1 0 0 0 0 0 1 1 0 0 0 0 1 0 1 1 0 1 1 0 0 1\n",
      " 1 1 0 1 1 1 1 1 1 1 1 1 0 1 0 1 0 0 1 1 0 0 0 1 1 0 0 0 1 1 0 1 1 1 1 0 1\n",
      " 1 0 0 0 1 1 1 0 0 0 1 0 1 0 1 1 1 1 0 0 0 1 0 1 1 1 0 1 1 0 1 1 1 1 0 1 0\n",
      " 0 1 0 0 1 0 1 0 0 0 1 0 1 1 0 0 0 1 0 0 1 0 0 1 0 0 0 1 0 0 0 1 1 0 1 1 1\n",
      " 1 0 1 0 0 0 0 0 1 1 1 1 1 0 0 0 0 0 1 0 1 1 0 0 0 1 0 1 1 1 0 0 1 1 0 1 1\n",
      " 0 0 1 1 0 1 1 1 1 1 1 0 0 0 0 1 0 0 1 1 0 1 1 1 0 1 1 0 1 0 1 0 0 1 0 0 1\n",
      " 0 0 0 1 1 1 1 0 1 1 0 0 0 1 1 0 1 1 0 1 1 1 1 1 1 0 0 0 0 1 0 0 0 0 0 0 0\n",
      " 1 0 0 0 1 1 1 1 0 1 1 1 0 0 0 0 0 1 1 0 0 1 1 1 1 0 0 1 1 1 1 1 1 1 0 1 0\n",
      " 1 0 0 0]\n",
      "\n",
      "----------------------------------------\n",
      "val data\n",
      "----------------------------------------\n",
      "[[ 1.46544762  0.16031256]\n",
      " [ 2.73592153  0.43530235]\n",
      " [ 5.00013245  0.40346429]\n",
      " [ 4.55357974 -0.10050192]\n",
      " [ 5.94984314 -0.41071394]\n",
      " [ 0.99373702  0.61322159]\n",
      " [ 3.35229006 -0.39958164]\n",
      " [ 3.54726378 -0.46107173]\n",
      " [ 3.76110592 -0.50749654]\n",
      " [ 3.12586897 -1.00361073]\n",
      " [ 5.05044825 -0.83716232]\n",
      " [ 4.07557966 -0.62876886]\n",
      " [ 2.94976367 -0.7220735 ]\n",
      " [ 1.65413187 -0.13066137]\n",
      " [ 3.45292166 -0.99407059]\n",
      " [ 5.7863168   0.80471307]\n",
      " [ 4.32086918 -0.6820786 ]\n",
      " [ 3.86802699 -0.53118068]\n",
      " [ 6.08821159 -0.04237077]\n",
      " [ 4.88063243  0.31983793]\n",
      " [ 0.03144737  0.98886275]\n",
      " [ 5.57247466 -0.80783206]\n",
      " [ 0.40881586  0.41361457]\n",
      " [ 5.02529035  0.30418673]\n",
      " [ 3.88689542 -0.75944674]\n",
      " [ 0.03773685  0.10895041]\n",
      " [ 4.42150077 -0.88442475]\n",
      " [ 2.52836886 -0.83524406]\n",
      " [ 2.2956583  -0.70213026]\n",
      " [ 1.80507926  1.01115978]\n",
      " [ 4.84289558 -1.10842752]\n",
      " [ 0.77989487  0.73743618]\n",
      " [ 4.83660611  0.05070972]\n",
      " [ 3.53468483 -0.36704433]\n",
      " [ 0.30189479  0.81051862]\n",
      " [ 2.42144779  0.75560743]\n",
      " [ 2.97492157 -0.95823717]\n",
      " [ 2.15728985  0.79396373]\n",
      " [ 0.22642109  0.1365694 ]\n",
      " [ 2.66673731 -0.85710275]\n",
      " [ 1.27047391  0.91634619]\n",
      " [ 0.17610529  1.15947402]\n",
      " [ 0.54089483  0.3786734 ]\n",
      " [ 5.09447457 -0.9165532 ]\n",
      " [ 3.65418485 -0.35247409]\n",
      " [ 0.71071065  0.73367089]\n",
      " [ 5.61650098  0.7085194 ]\n",
      " [ 4.30200075 -0.98921412]\n",
      " [ 0.7736054   0.72171485]\n",
      " [ 4.06929018 -0.78930795]\n",
      " [ 5.94355367 -0.38848045]\n",
      " [ 0.59121063  1.11447048]\n",
      " [ 4.52213237 -1.01173484]\n",
      " [ 6.14481686 -0.11431666]\n",
      " [ 4.76742188  0.05851876]\n",
      " [ 5.39636936 -0.84697998]\n",
      " [ 1.15726336  1.01126993]\n",
      " [ 5.13221142  0.2817713 ]\n",
      " [ 3.23907951 -0.22760813]\n",
      " [ 0.8490791   0.84316885]\n",
      " [ 1.58494765  0.07930885]\n",
      " [ 4.32715865 -0.96715045]\n",
      " [ 5.75486943  0.80286145]\n",
      " [ 2.57239519  0.59905821]\n",
      " [ 2.9183163   0.11255202]\n",
      " [ 1.90571086  1.1114192 ]\n",
      " [ 2.44660569  0.53355402]\n",
      " [ 0.28302637  0.38039485]\n",
      " [ 2.0943951   0.79772943]\n",
      " [ 5.15107985 -1.0084846 ]\n",
      " [ 4.54729027 -0.18689136]\n",
      " [ 1.91200033 -0.34767351]\n",
      " [ 3.86173752 -0.49801916]\n",
      " [ 5.1888167   0.51890403]\n",
      " [ 2.8680005   0.28034142]\n",
      " [ 1.67300029  0.98247308]\n",
      " [ 2.0440793  -0.51370656]\n",
      " [ 0.78618435  0.61806744]\n",
      " [ 4.82402716 -0.92747116]\n",
      " [ 3.03781632 -0.06305213]\n",
      " [ 3.82400067 -0.80931515]\n",
      " [ 2.27678987 -0.61078447]\n",
      " [ 4.15105336 -0.79948455]\n",
      " [ 5.15736932 -0.82250518]\n",
      " [ 2.46547411 -0.80233562]\n",
      " [ 2.51578991 -0.81036663]\n",
      " [ 5.37750094  0.51675278]\n",
      " [ 4.9875535   0.28321546]\n",
      " [ 0.71071065  0.72978729]\n",
      " [ 6.00015894 -0.14864427]\n",
      " [ 2.72963206  0.46373129]\n",
      " [ 3.17618476 -0.03072847]\n",
      " [ 1.35223708  0.23601133]\n",
      " [ 1.50318447  0.20449568]\n",
      " [ 2.50321096  0.57141644]\n",
      " [ 3.05668474 -0.89597362]\n",
      " [ 0.05660527  0.05237984]\n",
      " [ 0.29560531  1.02991259]\n",
      " [ 3.93721121 -0.5295586 ]\n",
      " [ 5.66681678 -0.57987648]]\n",
      "[1 0 1 1 0 1 0 0 0 1 0 1 1 1 1 1 0 0 0 1 1 0 0 1 1 0 0 1 1 0 0 1 1 0 1 0 1\n",
      " 0 0 1 0 1 0 0 0 1 1 0 0 0 0 1 0 0 1 0 0 1 0 0 1 0 1 0 0 0 0 0 0 0 1 1 0 1\n",
      " 0 0 1 1 0 0 1 1 0 0 1 1 1 1 0 0 0 0 1 1 0 1 0 1 0 0]\n"
     ]
    }
   ],
   "source": [
    "# 创建标签映射字典\n",
    "label_map = {'sin': 0, 'cos': 1}\n",
    "\n",
    "# 将数据转换为numpy数组并进行适当的预处理\n",
    "train_label = [label_map[item['curve']] for item in train_data]\n",
    "val_label = [label_map[item['curve']] for item in val_data]\n",
    "test_label = [label_map[item['curve']] for item in test_data]\n",
    "\n",
    "\n",
    "# 假设train_data和val_data是包含x和y值的列表\n",
    "# 将数据转换为numpy数组并进行适当的预处理\n",
    "train_x = np.array([[item['x'], item['y']] for item in train_data])\n",
    "# train_y = keras.utils.to_categorical(train_label, num_classes=2)\n",
    "train_y = np.array(train_label)\n",
    "\n",
    "x_validate = np.array([[item['x'], item['y']] for item in val_data])\n",
    "# y_validate = keras.utils.to_categorical(val_label, num_classes=2)\n",
    "y_validate = np.array(val_label)\n",
    "\n",
    "x_test = np.array([[item['x'], item['y']] for item in test_data])\n",
    "# y_test = keras.utils.to_categorical(test_label, num_classes=2)\n",
    "y_test = np.array(test_label)\n",
    "\n",
    "print_logo(train_x, train_y, x_validate, y_validate, x_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/500\n",
      "25/25 [==============================] - 1s 26ms/step - loss: 0.2573 - mae: 0.4978 - val_loss: 0.2469 - val_mae: 0.4939\n",
      "Epoch 2/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.2444 - mae: 0.4916 - val_loss: 0.2457 - val_mae: 0.4939\n",
      "Epoch 3/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.2415 - mae: 0.4894 - val_loss: 0.2426 - val_mae: 0.4911\n",
      "Epoch 4/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.2411 - mae: 0.4896 - val_loss: 0.2401 - val_mae: 0.4883\n",
      "Epoch 5/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.2382 - mae: 0.4863 - val_loss: 0.2382 - val_mae: 0.4864\n",
      "Epoch 6/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.2361 - mae: 0.4837 - val_loss: 0.2371 - val_mae: 0.4846\n",
      "Epoch 7/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.2339 - mae: 0.4807 - val_loss: 0.2336 - val_mae: 0.4805\n",
      "Epoch 8/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.2304 - mae: 0.4771 - val_loss: 0.2319 - val_mae: 0.4784\n",
      "Epoch 9/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.2280 - mae: 0.4738 - val_loss: 0.2294 - val_mae: 0.4742\n",
      "Epoch 10/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.2246 - mae: 0.4695 - val_loss: 0.2267 - val_mae: 0.4718\n",
      "Epoch 11/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.2228 - mae: 0.4674 - val_loss: 0.2239 - val_mae: 0.4684\n",
      "Epoch 12/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.2209 - mae: 0.4645 - val_loss: 0.2212 - val_mae: 0.4636\n",
      "Epoch 13/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.2197 - mae: 0.4624 - val_loss: 0.2183 - val_mae: 0.4597\n",
      "Epoch 14/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.2146 - mae: 0.4556 - val_loss: 0.2156 - val_mae: 0.4560\n",
      "Epoch 15/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.2128 - mae: 0.4525 - val_loss: 0.2132 - val_mae: 0.4524\n",
      "Epoch 16/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.2077 - mae: 0.4462 - val_loss: 0.2104 - val_mae: 0.4481\n",
      "Epoch 17/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.2065 - mae: 0.4445 - val_loss: 0.2084 - val_mae: 0.4423\n",
      "Epoch 18/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.2025 - mae: 0.4351 - val_loss: 0.2047 - val_mae: 0.4389\n",
      "Epoch 19/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.2036 - mae: 0.4376 - val_loss: 0.2030 - val_mae: 0.4364\n",
      "Epoch 20/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.1970 - mae: 0.4297 - val_loss: 0.2009 - val_mae: 0.4324\n",
      "Epoch 21/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.1981 - mae: 0.4290 - val_loss: 0.1975 - val_mae: 0.4255\n",
      "Epoch 22/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.1884 - mae: 0.4159 - val_loss: 0.1950 - val_mae: 0.4214\n",
      "Epoch 23/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.1930 - mae: 0.4209 - val_loss: 0.1931 - val_mae: 0.4187\n",
      "Epoch 24/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.1867 - mae: 0.4118 - val_loss: 0.1912 - val_mae: 0.4129\n",
      "Epoch 25/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.1855 - mae: 0.4076 - val_loss: 0.1914 - val_mae: 0.4080\n",
      "Epoch 26/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.1853 - mae: 0.4054 - val_loss: 0.1870 - val_mae: 0.4063\n",
      "Epoch 27/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.1796 - mae: 0.3985 - val_loss: 0.1855 - val_mae: 0.4037\n",
      "Epoch 28/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.1773 - mae: 0.3951 - val_loss: 0.1830 - val_mae: 0.3988\n",
      "Epoch 29/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.1754 - mae: 0.3905 - val_loss: 0.1806 - val_mae: 0.3941\n",
      "Epoch 30/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.1700 - mae: 0.3842 - val_loss: 0.1784 - val_mae: 0.3888\n",
      "Epoch 31/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.1701 - mae: 0.3828 - val_loss: 0.1761 - val_mae: 0.3845\n",
      "Epoch 32/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.1651 - mae: 0.3737 - val_loss: 0.1736 - val_mae: 0.3785\n",
      "Epoch 33/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.1659 - mae: 0.3730 - val_loss: 0.1709 - val_mae: 0.3739\n",
      "Epoch 34/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.1676 - mae: 0.3725 - val_loss: 0.1703 - val_mae: 0.3701\n",
      "Epoch 35/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.1669 - mae: 0.3699 - val_loss: 0.1699 - val_mae: 0.3648\n",
      "Epoch 36/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.1581 - mae: 0.3552 - val_loss: 0.1679 - val_mae: 0.3627\n",
      "Epoch 37/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.1558 - mae: 0.3527 - val_loss: 0.1666 - val_mae: 0.3572\n",
      "Epoch 38/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.1565 - mae: 0.3518 - val_loss: 0.1646 - val_mae: 0.3570\n",
      "Epoch 39/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.1617 - mae: 0.3571 - val_loss: 0.1650 - val_mae: 0.3514\n",
      "Epoch 40/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.1500 - mae: 0.3402 - val_loss: 0.1631 - val_mae: 0.3524\n",
      "Epoch 41/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.1614 - mae: 0.3535 - val_loss: 0.1607 - val_mae: 0.3471\n",
      "Epoch 42/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.1587 - mae: 0.3478 - val_loss: 0.1605 - val_mae: 0.3441\n",
      "Epoch 43/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.1495 - mae: 0.3367 - val_loss: 0.1590 - val_mae: 0.3426\n",
      "Epoch 44/500\n",
      "25/25 [==============================] - 0s 1ms/step - loss: 0.1527 - mae: 0.3411 - val_loss: 0.1580 - val_mae: 0.3387\n",
      "Epoch 45/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.1515 - mae: 0.3365 - val_loss: 0.1569 - val_mae: 0.3368\n",
      "Epoch 46/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.1573 - mae: 0.3392 - val_loss: 0.1561 - val_mae: 0.3335\n",
      "Epoch 47/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.1480 - mae: 0.3284 - val_loss: 0.1554 - val_mae: 0.3324\n",
      "Epoch 48/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.1439 - mae: 0.3214 - val_loss: 0.1540 - val_mae: 0.3288\n",
      "Epoch 49/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.1477 - mae: 0.3255 - val_loss: 0.1534 - val_mae: 0.3258\n",
      "Epoch 50/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.1581 - mae: 0.3353 - val_loss: 0.1533 - val_mae: 0.3225\n",
      "Epoch 51/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.1506 - mae: 0.3270 - val_loss: 0.1515 - val_mae: 0.3216\n",
      "Epoch 52/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.1441 - mae: 0.3196 - val_loss: 0.1506 - val_mae: 0.3201\n",
      "Epoch 53/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.1432 - mae: 0.3170 - val_loss: 0.1498 - val_mae: 0.3181\n",
      "Epoch 54/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.1431 - mae: 0.3176 - val_loss: 0.1487 - val_mae: 0.3149\n",
      "Epoch 55/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.1493 - mae: 0.3221 - val_loss: 0.1480 - val_mae: 0.3133\n",
      "Epoch 56/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.1361 - mae: 0.3068 - val_loss: 0.1469 - val_mae: 0.3108\n",
      "Epoch 57/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.1407 - mae: 0.3098 - val_loss: 0.1466 - val_mae: 0.3088\n",
      "Epoch 58/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.1454 - mae: 0.3147 - val_loss: 0.1468 - val_mae: 0.3068\n",
      "Epoch 59/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.1378 - mae: 0.3051 - val_loss: 0.1450 - val_mae: 0.3056\n",
      "Epoch 60/500\n",
      "25/25 [==============================] - 0s 1ms/step - loss: 0.1413 - mae: 0.3064 - val_loss: 0.1442 - val_mae: 0.3050\n",
      "Epoch 61/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.1450 - mae: 0.3099 - val_loss: 0.1436 - val_mae: 0.3042\n",
      "Epoch 62/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.1312 - mae: 0.2948 - val_loss: 0.1424 - val_mae: 0.3015\n",
      "Epoch 63/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.1424 - mae: 0.3055 - val_loss: 0.1421 - val_mae: 0.3010\n",
      "Epoch 64/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.1353 - mae: 0.2996 - val_loss: 0.1408 - val_mae: 0.2982\n",
      "Epoch 65/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.1319 - mae: 0.2949 - val_loss: 0.1408 - val_mae: 0.2992\n",
      "Epoch 66/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.1258 - mae: 0.2854 - val_loss: 0.1393 - val_mae: 0.2960\n",
      "Epoch 67/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.1354 - mae: 0.2952 - val_loss: 0.1385 - val_mae: 0.2952\n",
      "Epoch 68/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.1316 - mae: 0.2913 - val_loss: 0.1379 - val_mae: 0.2919\n",
      "Epoch 69/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.1286 - mae: 0.2873 - val_loss: 0.1367 - val_mae: 0.2911\n",
      "Epoch 70/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.1293 - mae: 0.2880 - val_loss: 0.1370 - val_mae: 0.2886\n",
      "Epoch 71/500\n",
      "25/25 [==============================] - 0s 1ms/step - loss: 0.1328 - mae: 0.2894 - val_loss: 0.1348 - val_mae: 0.2885\n",
      "Epoch 72/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.1291 - mae: 0.2872 - val_loss: 0.1339 - val_mae: 0.2890\n",
      "Epoch 73/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.1295 - mae: 0.2893 - val_loss: 0.1334 - val_mae: 0.2890\n",
      "Epoch 74/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.1242 - mae: 0.2823 - val_loss: 0.1318 - val_mae: 0.2862\n",
      "Epoch 75/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.1294 - mae: 0.2880 - val_loss: 0.1307 - val_mae: 0.2860\n",
      "Epoch 76/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.1226 - mae: 0.2826 - val_loss: 0.1297 - val_mae: 0.2842\n",
      "Epoch 77/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.1272 - mae: 0.2887 - val_loss: 0.1285 - val_mae: 0.2840\n",
      "Epoch 78/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.1214 - mae: 0.2789 - val_loss: 0.1274 - val_mae: 0.2843\n",
      "Epoch 79/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.1158 - mae: 0.2749 - val_loss: 0.1273 - val_mae: 0.2819\n",
      "Epoch 80/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.1213 - mae: 0.2824 - val_loss: 0.1258 - val_mae: 0.2811\n",
      "Epoch 81/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.1207 - mae: 0.2798 - val_loss: 0.1247 - val_mae: 0.2814\n",
      "Epoch 82/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.1209 - mae: 0.2838 - val_loss: 0.1240 - val_mae: 0.2827\n",
      "Epoch 83/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.1227 - mae: 0.2858 - val_loss: 0.1227 - val_mae: 0.2805\n",
      "Epoch 84/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.1153 - mae: 0.2733 - val_loss: 0.1225 - val_mae: 0.2778\n",
      "Epoch 85/500\n",
      "25/25 [==============================] - 0s 1ms/step - loss: 0.1219 - mae: 0.2847 - val_loss: 0.1204 - val_mae: 0.2795\n",
      "Epoch 86/500\n",
      "25/25 [==============================] - 0s 1ms/step - loss: 0.1144 - mae: 0.2731 - val_loss: 0.1192 - val_mae: 0.2773\n",
      "Epoch 87/500\n",
      "25/25 [==============================] - 0s 1ms/step - loss: 0.1160 - mae: 0.2776 - val_loss: 0.1187 - val_mae: 0.2773\n",
      "Epoch 88/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.1069 - mae: 0.2671 - val_loss: 0.1173 - val_mae: 0.2746\n",
      "Epoch 89/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.1085 - mae: 0.2658 - val_loss: 0.1156 - val_mae: 0.2711\n",
      "Epoch 90/500\n",
      "25/25 [==============================] - 0s 1ms/step - loss: 0.1072 - mae: 0.2648 - val_loss: 0.1140 - val_mae: 0.2671\n",
      "Epoch 91/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.1085 - mae: 0.2657 - val_loss: 0.1129 - val_mae: 0.2681\n",
      "Epoch 92/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.1015 - mae: 0.2559 - val_loss: 0.1126 - val_mae: 0.2676\n",
      "Epoch 93/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0995 - mae: 0.2511 - val_loss: 0.1120 - val_mae: 0.2667\n",
      "Epoch 94/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.1002 - mae: 0.2549 - val_loss: 0.1089 - val_mae: 0.2624\n",
      "Epoch 95/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0948 - mae: 0.2454 - val_loss: 0.1063 - val_mae: 0.2584\n",
      "Epoch 96/500\n",
      "25/25 [==============================] - 0s 3ms/step - loss: 0.0994 - mae: 0.2513 - val_loss: 0.1038 - val_mae: 0.2534\n",
      "Epoch 97/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0941 - mae: 0.2444 - val_loss: 0.1020 - val_mae: 0.2494\n",
      "Epoch 98/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0966 - mae: 0.2458 - val_loss: 0.1006 - val_mae: 0.2507\n",
      "Epoch 99/500\n",
      "25/25 [==============================] - 0s 1ms/step - loss: 0.0885 - mae: 0.2347 - val_loss: 0.0972 - val_mae: 0.2441\n",
      "Epoch 100/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0899 - mae: 0.2360 - val_loss: 0.0932 - val_mae: 0.2376\n",
      "Epoch 101/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0825 - mae: 0.2245 - val_loss: 0.0895 - val_mae: 0.2374\n",
      "Epoch 102/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0824 - mae: 0.2281 - val_loss: 0.0856 - val_mae: 0.2313\n",
      "Epoch 103/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0799 - mae: 0.2229 - val_loss: 0.0809 - val_mae: 0.2262\n",
      "Epoch 104/500\n",
      "25/25 [==============================] - 0s 1ms/step - loss: 0.0767 - mae: 0.2198 - val_loss: 0.0763 - val_mae: 0.2184\n",
      "Epoch 105/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0759 - mae: 0.2155 - val_loss: 0.0719 - val_mae: 0.2110\n",
      "Epoch 106/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0722 - mae: 0.2082 - val_loss: 0.0701 - val_mae: 0.2072\n",
      "Epoch 107/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0656 - mae: 0.1969 - val_loss: 0.0660 - val_mae: 0.1994\n",
      "Epoch 108/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0687 - mae: 0.2008 - val_loss: 0.0672 - val_mae: 0.1973\n",
      "Epoch 109/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0619 - mae: 0.1903 - val_loss: 0.0608 - val_mae: 0.1910\n",
      "Epoch 110/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0626 - mae: 0.1898 - val_loss: 0.0600 - val_mae: 0.1865\n",
      "Epoch 111/500\n",
      "25/25 [==============================] - 0s 1ms/step - loss: 0.0595 - mae: 0.1827 - val_loss: 0.0571 - val_mae: 0.1811\n",
      "Epoch 112/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0597 - mae: 0.1821 - val_loss: 0.0561 - val_mae: 0.1778\n",
      "Epoch 113/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0565 - mae: 0.1765 - val_loss: 0.0553 - val_mae: 0.1753\n",
      "Epoch 114/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0567 - mae: 0.1740 - val_loss: 0.0531 - val_mae: 0.1703\n",
      "Epoch 115/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0511 - mae: 0.1638 - val_loss: 0.0526 - val_mae: 0.1688\n",
      "Epoch 116/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0569 - mae: 0.1717 - val_loss: 0.0528 - val_mae: 0.1649\n",
      "Epoch 117/500\n",
      "25/25 [==============================] - 0s 1ms/step - loss: 0.0532 - mae: 0.1633 - val_loss: 0.0505 - val_mae: 0.1626\n",
      "Epoch 118/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0494 - mae: 0.1565 - val_loss: 0.0485 - val_mae: 0.1591\n",
      "Epoch 119/500\n",
      "25/25 [==============================] - 0s 1ms/step - loss: 0.0478 - mae: 0.1526 - val_loss: 0.0484 - val_mae: 0.1576\n",
      "Epoch 120/500\n",
      "25/25 [==============================] - 0s 1ms/step - loss: 0.0463 - mae: 0.1497 - val_loss: 0.0472 - val_mae: 0.1542\n",
      "Epoch 121/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0498 - mae: 0.1550 - val_loss: 0.0463 - val_mae: 0.1510\n",
      "Epoch 122/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0460 - mae: 0.1445 - val_loss: 0.0452 - val_mae: 0.1490\n",
      "Epoch 123/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0453 - mae: 0.1456 - val_loss: 0.0459 - val_mae: 0.1485\n",
      "Epoch 124/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0462 - mae: 0.1457 - val_loss: 0.0449 - val_mae: 0.1456\n",
      "Epoch 125/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0409 - mae: 0.1342 - val_loss: 0.0458 - val_mae: 0.1463\n",
      "Epoch 126/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0469 - mae: 0.1450 - val_loss: 0.0428 - val_mae: 0.1405\n",
      "Epoch 127/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0466 - mae: 0.1427 - val_loss: 0.0424 - val_mae: 0.1391\n",
      "Epoch 128/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0445 - mae: 0.1385 - val_loss: 0.0422 - val_mae: 0.1375\n",
      "Epoch 129/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0439 - mae: 0.1370 - val_loss: 0.0415 - val_mae: 0.1365\n",
      "Epoch 130/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0410 - mae: 0.1309 - val_loss: 0.0402 - val_mae: 0.1331\n",
      "Epoch 131/500\n",
      "25/25 [==============================] - 0s 1ms/step - loss: 0.0462 - mae: 0.1401 - val_loss: 0.0406 - val_mae: 0.1330\n",
      "Epoch 132/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0431 - mae: 0.1330 - val_loss: 0.0412 - val_mae: 0.1319\n",
      "Epoch 133/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0442 - mae: 0.1331 - val_loss: 0.0394 - val_mae: 0.1288\n",
      "Epoch 134/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0425 - mae: 0.1291 - val_loss: 0.0383 - val_mae: 0.1272\n",
      "Epoch 135/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0406 - mae: 0.1277 - val_loss: 0.0385 - val_mae: 0.1271\n",
      "Epoch 136/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0423 - mae: 0.1278 - val_loss: 0.0380 - val_mae: 0.1241\n",
      "Epoch 137/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0381 - mae: 0.1220 - val_loss: 0.0378 - val_mae: 0.1239\n",
      "Epoch 138/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0407 - mae: 0.1258 - val_loss: 0.0367 - val_mae: 0.1215\n",
      "Epoch 139/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0370 - mae: 0.1174 - val_loss: 0.0371 - val_mae: 0.1226\n",
      "Epoch 140/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0392 - mae: 0.1217 - val_loss: 0.0372 - val_mae: 0.1209\n",
      "Epoch 141/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0410 - mae: 0.1242 - val_loss: 0.0363 - val_mae: 0.1186\n",
      "Epoch 142/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0404 - mae: 0.1213 - val_loss: 0.0358 - val_mae: 0.1174\n",
      "Epoch 143/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0394 - mae: 0.1175 - val_loss: 0.0353 - val_mae: 0.1169\n",
      "Epoch 144/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0417 - mae: 0.1230 - val_loss: 0.0354 - val_mae: 0.1162\n",
      "Epoch 145/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0388 - mae: 0.1177 - val_loss: 0.0353 - val_mae: 0.1150\n",
      "Epoch 146/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0381 - mae: 0.1135 - val_loss: 0.0348 - val_mae: 0.1151\n",
      "Epoch 147/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0388 - mae: 0.1180 - val_loss: 0.0350 - val_mae: 0.1130\n",
      "Epoch 148/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0374 - mae: 0.1147 - val_loss: 0.0342 - val_mae: 0.1112\n",
      "Epoch 149/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0369 - mae: 0.1121 - val_loss: 0.0340 - val_mae: 0.1105\n",
      "Epoch 150/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0351 - mae: 0.1076 - val_loss: 0.0374 - val_mae: 0.1150\n",
      "Epoch 151/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0382 - mae: 0.1132 - val_loss: 0.0336 - val_mae: 0.1098\n",
      "Epoch 152/500\n",
      "25/25 [==============================] - 0s 3ms/step - loss: 0.0392 - mae: 0.1170 - val_loss: 0.0335 - val_mae: 0.1090\n",
      "Epoch 153/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0358 - mae: 0.1076 - val_loss: 0.0337 - val_mae: 0.1086\n",
      "Epoch 154/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0376 - mae: 0.1124 - val_loss: 0.0328 - val_mae: 0.1069\n",
      "Epoch 155/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0345 - mae: 0.1043 - val_loss: 0.0336 - val_mae: 0.1079\n",
      "Epoch 156/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0357 - mae: 0.1076 - val_loss: 0.0324 - val_mae: 0.1059\n",
      "Epoch 157/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0347 - mae: 0.1059 - val_loss: 0.0321 - val_mae: 0.1046\n",
      "Epoch 158/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0374 - mae: 0.1096 - val_loss: 0.0321 - val_mae: 0.1038\n",
      "Epoch 159/500\n",
      "25/25 [==============================] - 0s 3ms/step - loss: 0.0347 - mae: 0.1056 - val_loss: 0.0322 - val_mae: 0.1032\n",
      "Epoch 160/500\n",
      "25/25 [==============================] - 0s 3ms/step - loss: 0.0356 - mae: 0.1053 - val_loss: 0.0329 - val_mae: 0.1046\n",
      "Epoch 161/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0354 - mae: 0.1031 - val_loss: 0.0319 - val_mae: 0.1029\n",
      "Epoch 162/500\n",
      "25/25 [==============================] - 0s 3ms/step - loss: 0.0341 - mae: 0.1023 - val_loss: 0.0318 - val_mae: 0.1006\n",
      "Epoch 163/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0359 - mae: 0.1039 - val_loss: 0.0314 - val_mae: 0.1012\n",
      "Epoch 164/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0320 - mae: 0.0964 - val_loss: 0.0310 - val_mae: 0.1002\n",
      "Epoch 165/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0363 - mae: 0.1060 - val_loss: 0.0309 - val_mae: 0.0997\n",
      "Epoch 166/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0370 - mae: 0.1043 - val_loss: 0.0308 - val_mae: 0.0990\n",
      "Epoch 167/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0340 - mae: 0.1000 - val_loss: 0.0303 - val_mae: 0.0974\n",
      "Epoch 168/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0325 - mae: 0.0949 - val_loss: 0.0308 - val_mae: 0.0985\n",
      "Epoch 169/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0367 - mae: 0.1059 - val_loss: 0.0311 - val_mae: 0.0983\n",
      "Epoch 170/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0386 - mae: 0.1073 - val_loss: 0.0299 - val_mae: 0.0967\n",
      "Epoch 171/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0295 - mae: 0.0916 - val_loss: 0.0310 - val_mae: 0.0970\n",
      "Epoch 172/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0337 - mae: 0.0979 - val_loss: 0.0299 - val_mae: 0.0956\n",
      "Epoch 173/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0324 - mae: 0.0952 - val_loss: 0.0304 - val_mae: 0.0961\n",
      "Epoch 174/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0336 - mae: 0.0961 - val_loss: 0.0296 - val_mae: 0.0948\n",
      "Epoch 175/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0350 - mae: 0.0997 - val_loss: 0.0294 - val_mae: 0.0939\n",
      "Epoch 176/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0332 - mae: 0.0956 - val_loss: 0.0297 - val_mae: 0.0947\n",
      "Epoch 177/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0320 - mae: 0.0938 - val_loss: 0.0295 - val_mae: 0.0936\n",
      "Epoch 178/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0299 - mae: 0.0884 - val_loss: 0.0292 - val_mae: 0.0925\n",
      "Epoch 179/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0320 - mae: 0.0949 - val_loss: 0.0309 - val_mae: 0.0944\n",
      "Epoch 180/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0368 - mae: 0.0994 - val_loss: 0.0289 - val_mae: 0.0921\n",
      "Epoch 181/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0327 - mae: 0.0943 - val_loss: 0.0291 - val_mae: 0.0924\n",
      "Epoch 182/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0327 - mae: 0.0946 - val_loss: 0.0284 - val_mae: 0.0907\n",
      "Epoch 183/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0281 - mae: 0.0855 - val_loss: 0.0292 - val_mae: 0.0909\n",
      "Epoch 184/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0318 - mae: 0.0923 - val_loss: 0.0289 - val_mae: 0.0902\n",
      "Epoch 185/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0358 - mae: 0.1004 - val_loss: 0.0286 - val_mae: 0.0898\n",
      "Epoch 186/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0315 - mae: 0.0889 - val_loss: 0.0284 - val_mae: 0.0895\n",
      "Epoch 187/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0342 - mae: 0.0971 - val_loss: 0.0278 - val_mae: 0.0884\n",
      "Epoch 188/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0317 - mae: 0.0906 - val_loss: 0.0279 - val_mae: 0.0882\n",
      "Epoch 189/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0319 - mae: 0.0897 - val_loss: 0.0278 - val_mae: 0.0877\n",
      "Epoch 190/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0326 - mae: 0.0930 - val_loss: 0.0302 - val_mae: 0.0902\n",
      "Epoch 191/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0353 - mae: 0.0950 - val_loss: 0.0283 - val_mae: 0.0888\n",
      "Epoch 192/500\n",
      "25/25 [==============================] - 0s 3ms/step - loss: 0.0322 - mae: 0.0911 - val_loss: 0.0281 - val_mae: 0.0870\n",
      "Epoch 193/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0312 - mae: 0.0887 - val_loss: 0.0279 - val_mae: 0.0874\n",
      "Epoch 194/500\n",
      "25/25 [==============================] - 0s 3ms/step - loss: 0.0316 - mae: 0.0894 - val_loss: 0.0272 - val_mae: 0.0856\n",
      "Epoch 195/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0349 - mae: 0.0955 - val_loss: 0.0308 - val_mae: 0.0899\n",
      "Epoch 196/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0329 - mae: 0.0900 - val_loss: 0.0290 - val_mae: 0.0869\n",
      "Epoch 197/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0326 - mae: 0.0896 - val_loss: 0.0273 - val_mae: 0.0854\n",
      "Epoch 198/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0287 - mae: 0.0819 - val_loss: 0.0270 - val_mae: 0.0853\n",
      "Epoch 199/500\n",
      "25/25 [==============================] - 0s 1ms/step - loss: 0.0304 - mae: 0.0879 - val_loss: 0.0269 - val_mae: 0.0840\n",
      "Epoch 200/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0334 - mae: 0.0894 - val_loss: 0.0284 - val_mae: 0.0851\n",
      "Epoch 201/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0326 - mae: 0.0894 - val_loss: 0.0280 - val_mae: 0.0851\n",
      "Epoch 202/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0309 - mae: 0.0903 - val_loss: 0.0282 - val_mae: 0.0844\n",
      "Epoch 203/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0337 - mae: 0.0882 - val_loss: 0.0265 - val_mae: 0.0827\n",
      "Epoch 204/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0279 - mae: 0.0812 - val_loss: 0.0265 - val_mae: 0.0821\n",
      "Epoch 205/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0312 - mae: 0.0854 - val_loss: 0.0267 - val_mae: 0.0822\n",
      "Epoch 206/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0345 - mae: 0.0923 - val_loss: 0.0271 - val_mae: 0.0829\n",
      "Epoch 207/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0326 - mae: 0.0851 - val_loss: 0.0259 - val_mae: 0.0815\n",
      "Epoch 208/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0305 - mae: 0.0845 - val_loss: 0.0265 - val_mae: 0.0813\n",
      "Epoch 209/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0331 - mae: 0.0892 - val_loss: 0.0260 - val_mae: 0.0805\n",
      "Epoch 210/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0302 - mae: 0.0842 - val_loss: 0.0261 - val_mae: 0.0813\n",
      "Epoch 211/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0330 - mae: 0.0886 - val_loss: 0.0279 - val_mae: 0.0818\n",
      "Epoch 212/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0300 - mae: 0.0842 - val_loss: 0.0269 - val_mae: 0.0820\n",
      "Epoch 213/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0338 - mae: 0.0910 - val_loss: 0.0268 - val_mae: 0.0810\n",
      "Epoch 214/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0285 - mae: 0.0782 - val_loss: 0.0260 - val_mae: 0.0801\n",
      "Epoch 215/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0312 - mae: 0.0834 - val_loss: 0.0268 - val_mae: 0.0793\n",
      "Epoch 216/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0279 - mae: 0.0801 - val_loss: 0.0259 - val_mae: 0.0796\n",
      "Epoch 217/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0293 - mae: 0.0821 - val_loss: 0.0249 - val_mae: 0.0777\n",
      "Epoch 218/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0325 - mae: 0.0865 - val_loss: 0.0258 - val_mae: 0.0785\n",
      "Epoch 219/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0298 - mae: 0.0818 - val_loss: 0.0254 - val_mae: 0.0778\n",
      "Epoch 220/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0308 - mae: 0.0823 - val_loss: 0.0254 - val_mae: 0.0774\n",
      "Epoch 221/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0297 - mae: 0.0793 - val_loss: 0.0257 - val_mae: 0.0778\n",
      "Epoch 222/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0300 - mae: 0.0827 - val_loss: 0.0251 - val_mae: 0.0766\n",
      "Epoch 223/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0293 - mae: 0.0792 - val_loss: 0.0256 - val_mae: 0.0775\n",
      "Epoch 224/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0262 - mae: 0.0758 - val_loss: 0.0261 - val_mae: 0.0768\n",
      "Epoch 225/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0296 - mae: 0.0820 - val_loss: 0.0253 - val_mae: 0.0771\n",
      "Epoch 226/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0295 - mae: 0.0789 - val_loss: 0.0252 - val_mae: 0.0757\n",
      "Epoch 227/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0290 - mae: 0.0790 - val_loss: 0.0260 - val_mae: 0.0771\n",
      "Epoch 228/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0304 - mae: 0.0807 - val_loss: 0.0248 - val_mae: 0.0761\n",
      "Epoch 229/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0317 - mae: 0.0830 - val_loss: 0.0246 - val_mae: 0.0752\n",
      "Epoch 230/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0309 - mae: 0.0814 - val_loss: 0.0253 - val_mae: 0.0755\n",
      "Epoch 231/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0299 - mae: 0.0786 - val_loss: 0.0246 - val_mae: 0.0753\n",
      "Epoch 232/500\n",
      "25/25 [==============================] - 0s 3ms/step - loss: 0.0294 - mae: 0.0783 - val_loss: 0.0243 - val_mae: 0.0747\n",
      "Epoch 233/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0303 - mae: 0.0806 - val_loss: 0.0244 - val_mae: 0.0741\n",
      "Epoch 234/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0306 - mae: 0.0804 - val_loss: 0.0259 - val_mae: 0.0757\n",
      "Epoch 235/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0285 - mae: 0.0784 - val_loss: 0.0240 - val_mae: 0.0738\n",
      "Epoch 236/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0294 - mae: 0.0781 - val_loss: 0.0267 - val_mae: 0.0765\n",
      "Epoch 237/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0323 - mae: 0.0832 - val_loss: 0.0248 - val_mae: 0.0743\n",
      "Epoch 238/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0276 - mae: 0.0767 - val_loss: 0.0245 - val_mae: 0.0739\n",
      "Epoch 239/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0277 - mae: 0.0754 - val_loss: 0.0243 - val_mae: 0.0738\n",
      "Epoch 240/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0313 - mae: 0.0809 - val_loss: 0.0246 - val_mae: 0.0731\n",
      "Epoch 241/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0306 - mae: 0.0791 - val_loss: 0.0238 - val_mae: 0.0726\n",
      "Epoch 242/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0284 - mae: 0.0772 - val_loss: 0.0252 - val_mae: 0.0748\n",
      "Epoch 243/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0315 - mae: 0.0807 - val_loss: 0.0245 - val_mae: 0.0726\n",
      "Epoch 244/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0313 - mae: 0.0818 - val_loss: 0.0235 - val_mae: 0.0721\n",
      "Epoch 245/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0256 - mae: 0.0714 - val_loss: 0.0245 - val_mae: 0.0725\n",
      "Epoch 246/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0272 - mae: 0.0734 - val_loss: 0.0240 - val_mae: 0.0726\n",
      "Epoch 247/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0331 - mae: 0.0843 - val_loss: 0.0233 - val_mae: 0.0712\n",
      "Epoch 248/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0254 - mae: 0.0690 - val_loss: 0.0248 - val_mae: 0.0718\n",
      "Epoch 249/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0283 - mae: 0.0734 - val_loss: 0.0240 - val_mae: 0.0716\n",
      "Epoch 250/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0271 - mae: 0.0720 - val_loss: 0.0236 - val_mae: 0.0709\n",
      "Epoch 251/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0295 - mae: 0.0764 - val_loss: 0.0239 - val_mae: 0.0708\n",
      "Epoch 252/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0286 - mae: 0.0736 - val_loss: 0.0245 - val_mae: 0.0715\n",
      "Epoch 253/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0280 - mae: 0.0730 - val_loss: 0.0244 - val_mae: 0.0711\n",
      "Epoch 254/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0297 - mae: 0.0773 - val_loss: 0.0240 - val_mae: 0.0713\n",
      "Epoch 255/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0261 - mae: 0.0707 - val_loss: 0.0234 - val_mae: 0.0696\n",
      "Epoch 256/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0301 - mae: 0.0768 - val_loss: 0.0238 - val_mae: 0.0711\n",
      "Epoch 257/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0296 - mae: 0.0755 - val_loss: 0.0235 - val_mae: 0.0696\n",
      "Epoch 258/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0254 - mae: 0.0689 - val_loss: 0.0236 - val_mae: 0.0692\n",
      "Epoch 259/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0299 - mae: 0.0750 - val_loss: 0.0231 - val_mae: 0.0689\n",
      "Epoch 260/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0288 - mae: 0.0742 - val_loss: 0.0231 - val_mae: 0.0695\n",
      "Epoch 261/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0277 - mae: 0.0729 - val_loss: 0.0237 - val_mae: 0.0692\n",
      "Epoch 262/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0298 - mae: 0.0799 - val_loss: 0.0232 - val_mae: 0.0695\n",
      "Epoch 263/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0314 - mae: 0.0796 - val_loss: 0.0231 - val_mae: 0.0690\n",
      "Epoch 264/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0296 - mae: 0.0756 - val_loss: 0.0228 - val_mae: 0.0678\n",
      "Epoch 265/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0266 - mae: 0.0712 - val_loss: 0.0246 - val_mae: 0.0701\n",
      "Epoch 266/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0286 - mae: 0.0754 - val_loss: 0.0226 - val_mae: 0.0683\n",
      "Epoch 267/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0287 - mae: 0.0735 - val_loss: 0.0238 - val_mae: 0.0691\n",
      "Epoch 268/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0266 - mae: 0.0713 - val_loss: 0.0242 - val_mae: 0.0686\n",
      "Epoch 269/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0295 - mae: 0.0746 - val_loss: 0.0231 - val_mae: 0.0684\n",
      "Epoch 270/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0293 - mae: 0.0748 - val_loss: 0.0232 - val_mae: 0.0679\n",
      "Epoch 271/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0292 - mae: 0.0729 - val_loss: 0.0254 - val_mae: 0.0695\n",
      "Epoch 272/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0285 - mae: 0.0717 - val_loss: 0.0239 - val_mae: 0.0683\n",
      "Epoch 273/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0257 - mae: 0.0687 - val_loss: 0.0226 - val_mae: 0.0677\n",
      "Epoch 274/500\n",
      "25/25 [==============================] - 0s 1ms/step - loss: 0.0273 - mae: 0.0721 - val_loss: 0.0236 - val_mae: 0.0689\n",
      "Epoch 275/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0299 - mae: 0.0754 - val_loss: 0.0226 - val_mae: 0.0666\n",
      "Epoch 276/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0277 - mae: 0.0711 - val_loss: 0.0226 - val_mae: 0.0670\n",
      "Epoch 277/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0307 - mae: 0.0744 - val_loss: 0.0237 - val_mae: 0.0676\n",
      "Epoch 278/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0278 - mae: 0.0722 - val_loss: 0.0224 - val_mae: 0.0661\n",
      "Epoch 279/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0285 - mae: 0.0724 - val_loss: 0.0231 - val_mae: 0.0672\n",
      "Epoch 280/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0328 - mae: 0.0804 - val_loss: 0.0233 - val_mae: 0.0661\n",
      "Epoch 281/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0282 - mae: 0.0719 - val_loss: 0.0228 - val_mae: 0.0672\n",
      "Epoch 282/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0287 - mae: 0.0719 - val_loss: 0.0252 - val_mae: 0.0677\n",
      "Epoch 283/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0282 - mae: 0.0716 - val_loss: 0.0231 - val_mae: 0.0661\n",
      "Epoch 284/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0301 - mae: 0.0732 - val_loss: 0.0218 - val_mae: 0.0649\n",
      "Epoch 285/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0307 - mae: 0.0746 - val_loss: 0.0217 - val_mae: 0.0651\n",
      "Epoch 286/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0247 - mae: 0.0641 - val_loss: 0.0250 - val_mae: 0.0670\n",
      "Epoch 287/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0291 - mae: 0.0695 - val_loss: 0.0250 - val_mae: 0.0670\n",
      "Epoch 288/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0306 - mae: 0.0753 - val_loss: 0.0236 - val_mae: 0.0660\n",
      "Epoch 289/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0264 - mae: 0.0664 - val_loss: 0.0221 - val_mae: 0.0645\n",
      "Epoch 290/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0264 - mae: 0.0683 - val_loss: 0.0227 - val_mae: 0.0654\n",
      "Epoch 291/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0279 - mae: 0.0728 - val_loss: 0.0221 - val_mae: 0.0645\n",
      "Epoch 292/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0303 - mae: 0.0750 - val_loss: 0.0227 - val_mae: 0.0644\n",
      "Epoch 293/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0307 - mae: 0.0745 - val_loss: 0.0240 - val_mae: 0.0656\n",
      "Epoch 294/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0299 - mae: 0.0735 - val_loss: 0.0230 - val_mae: 0.0644\n",
      "Epoch 295/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0290 - mae: 0.0708 - val_loss: 0.0215 - val_mae: 0.0637\n",
      "Epoch 296/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0264 - mae: 0.0661 - val_loss: 0.0230 - val_mae: 0.0649\n",
      "Epoch 297/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0266 - mae: 0.0672 - val_loss: 0.0220 - val_mae: 0.0641\n",
      "Epoch 298/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0282 - mae: 0.0708 - val_loss: 0.0254 - val_mae: 0.0667\n",
      "Epoch 299/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0285 - mae: 0.0696 - val_loss: 0.0216 - val_mae: 0.0634\n",
      "Epoch 300/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0270 - mae: 0.0694 - val_loss: 0.0222 - val_mae: 0.0630\n",
      "Epoch 301/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0307 - mae: 0.0729 - val_loss: 0.0218 - val_mae: 0.0644\n",
      "Epoch 302/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0285 - mae: 0.0715 - val_loss: 0.0231 - val_mae: 0.0641\n",
      "Epoch 303/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0248 - mae: 0.0633 - val_loss: 0.0235 - val_mae: 0.0643\n",
      "Epoch 304/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0301 - mae: 0.0715 - val_loss: 0.0223 - val_mae: 0.0643\n",
      "Epoch 305/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0265 - mae: 0.0658 - val_loss: 0.0214 - val_mae: 0.0627\n",
      "Epoch 306/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0264 - mae: 0.0664 - val_loss: 0.0223 - val_mae: 0.0629\n",
      "Epoch 307/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0276 - mae: 0.0678 - val_loss: 0.0217 - val_mae: 0.0630\n",
      "Epoch 308/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0253 - mae: 0.0640 - val_loss: 0.0230 - val_mae: 0.0631\n",
      "Epoch 309/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0247 - mae: 0.0651 - val_loss: 0.0229 - val_mae: 0.0639\n",
      "Epoch 310/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0230 - mae: 0.0614 - val_loss: 0.0212 - val_mae: 0.0623\n",
      "Epoch 311/500\n",
      "25/25 [==============================] - 0s 1ms/step - loss: 0.0300 - mae: 0.0711 - val_loss: 0.0272 - val_mae: 0.0668\n",
      "Epoch 312/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0283 - mae: 0.0674 - val_loss: 0.0217 - val_mae: 0.0623\n",
      "Epoch 313/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0293 - mae: 0.0723 - val_loss: 0.0223 - val_mae: 0.0627\n",
      "Epoch 314/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0289 - mae: 0.0715 - val_loss: 0.0218 - val_mae: 0.0617\n",
      "Epoch 315/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0290 - mae: 0.0714 - val_loss: 0.0222 - val_mae: 0.0629\n",
      "Epoch 316/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0313 - mae: 0.0743 - val_loss: 0.0228 - val_mae: 0.0623\n",
      "Epoch 317/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0280 - mae: 0.0690 - val_loss: 0.0215 - val_mae: 0.0618\n",
      "Epoch 318/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0319 - mae: 0.0734 - val_loss: 0.0210 - val_mae: 0.0615\n",
      "Epoch 319/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0260 - mae: 0.0651 - val_loss: 0.0213 - val_mae: 0.0616\n",
      "Epoch 320/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0300 - mae: 0.0727 - val_loss: 0.0223 - val_mae: 0.0619\n",
      "Epoch 321/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0301 - mae: 0.0708 - val_loss: 0.0213 - val_mae: 0.0613\n",
      "Epoch 322/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0241 - mae: 0.0614 - val_loss: 0.0237 - val_mae: 0.0631\n",
      "Epoch 323/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0271 - mae: 0.0667 - val_loss: 0.0250 - val_mae: 0.0636\n",
      "Epoch 324/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0262 - mae: 0.0674 - val_loss: 0.0211 - val_mae: 0.0611\n",
      "Epoch 325/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0270 - mae: 0.0684 - val_loss: 0.0211 - val_mae: 0.0606\n",
      "Epoch 326/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0298 - mae: 0.0720 - val_loss: 0.0208 - val_mae: 0.0601\n",
      "Epoch 327/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0301 - mae: 0.0693 - val_loss: 0.0218 - val_mae: 0.0616\n",
      "Epoch 328/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0281 - mae: 0.0680 - val_loss: 0.0218 - val_mae: 0.0620\n",
      "Epoch 329/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0244 - mae: 0.0597 - val_loss: 0.0222 - val_mae: 0.0613\n",
      "Epoch 330/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0250 - mae: 0.0625 - val_loss: 0.0214 - val_mae: 0.0607\n",
      "Epoch 331/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0256 - mae: 0.0627 - val_loss: 0.0211 - val_mae: 0.0598\n",
      "Epoch 332/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0314 - mae: 0.0724 - val_loss: 0.0214 - val_mae: 0.0608\n",
      "Epoch 333/500\n",
      "25/25 [==============================] - 0s 1ms/step - loss: 0.0307 - mae: 0.0730 - val_loss: 0.0209 - val_mae: 0.0602\n",
      "Epoch 334/500\n",
      "25/25 [==============================] - 0s 1ms/step - loss: 0.0259 - mae: 0.0643 - val_loss: 0.0209 - val_mae: 0.0602\n",
      "Epoch 335/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0273 - mae: 0.0656 - val_loss: 0.0232 - val_mae: 0.0627\n",
      "Epoch 336/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0290 - mae: 0.0712 - val_loss: 0.0264 - val_mae: 0.0636\n",
      "Epoch 337/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0287 - mae: 0.0676 - val_loss: 0.0212 - val_mae: 0.0606\n",
      "Epoch 338/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0267 - mae: 0.0678 - val_loss: 0.0211 - val_mae: 0.0592\n",
      "Epoch 339/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0295 - mae: 0.0698 - val_loss: 0.0215 - val_mae: 0.0604\n",
      "Epoch 340/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0264 - mae: 0.0664 - val_loss: 0.0206 - val_mae: 0.0593\n",
      "Epoch 341/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0246 - mae: 0.0602 - val_loss: 0.0212 - val_mae: 0.0605\n",
      "Epoch 342/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0308 - mae: 0.0713 - val_loss: 0.0226 - val_mae: 0.0602\n",
      "Epoch 343/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0259 - mae: 0.0652 - val_loss: 0.0209 - val_mae: 0.0594\n",
      "Epoch 344/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0265 - mae: 0.0646 - val_loss: 0.0238 - val_mae: 0.0610\n",
      "Epoch 345/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0270 - mae: 0.0663 - val_loss: 0.0209 - val_mae: 0.0591\n",
      "Epoch 346/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0261 - mae: 0.0643 - val_loss: 0.0207 - val_mae: 0.0589\n",
      "Epoch 347/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0239 - mae: 0.0621 - val_loss: 0.0212 - val_mae: 0.0597\n",
      "Epoch 348/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0233 - mae: 0.0590 - val_loss: 0.0202 - val_mae: 0.0586\n",
      "Epoch 349/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0272 - mae: 0.0649 - val_loss: 0.0242 - val_mae: 0.0616\n",
      "Epoch 350/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0285 - mae: 0.0662 - val_loss: 0.0214 - val_mae: 0.0597\n",
      "Epoch 351/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0273 - mae: 0.0648 - val_loss: 0.0225 - val_mae: 0.0601\n",
      "Epoch 352/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0268 - mae: 0.0665 - val_loss: 0.0216 - val_mae: 0.0598\n",
      "Epoch 353/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0280 - mae: 0.0706 - val_loss: 0.0221 - val_mae: 0.0600\n",
      "Epoch 354/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0248 - mae: 0.0611 - val_loss: 0.0221 - val_mae: 0.0592\n",
      "Epoch 355/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0301 - mae: 0.0687 - val_loss: 0.0204 - val_mae: 0.0577\n",
      "Epoch 356/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0296 - mae: 0.0686 - val_loss: 0.0211 - val_mae: 0.0587\n",
      "Epoch 357/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0282 - mae: 0.0647 - val_loss: 0.0207 - val_mae: 0.0583\n",
      "Epoch 358/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0269 - mae: 0.0659 - val_loss: 0.0207 - val_mae: 0.0586\n",
      "Epoch 359/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0300 - mae: 0.0693 - val_loss: 0.0210 - val_mae: 0.0584\n",
      "Epoch 360/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0253 - mae: 0.0602 - val_loss: 0.0207 - val_mae: 0.0583\n",
      "Epoch 361/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0277 - mae: 0.0682 - val_loss: 0.0207 - val_mae: 0.0576\n",
      "Epoch 362/500\n",
      "25/25 [==============================] - 0s 1ms/step - loss: 0.0247 - mae: 0.0624 - val_loss: 0.0206 - val_mae: 0.0585\n",
      "Epoch 363/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0281 - mae: 0.0696 - val_loss: 0.0212 - val_mae: 0.0580\n",
      "Epoch 364/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0276 - mae: 0.0648 - val_loss: 0.0227 - val_mae: 0.0589\n",
      "Epoch 365/500\n",
      "25/25 [==============================] - 0s 1ms/step - loss: 0.0212 - mae: 0.0538 - val_loss: 0.0217 - val_mae: 0.0593\n",
      "Epoch 366/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0326 - mae: 0.0748 - val_loss: 0.0213 - val_mae: 0.0583\n",
      "Epoch 367/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0278 - mae: 0.0646 - val_loss: 0.0210 - val_mae: 0.0587\n",
      "Epoch 368/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0270 - mae: 0.0659 - val_loss: 0.0217 - val_mae: 0.0581\n",
      "Epoch 369/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0237 - mae: 0.0590 - val_loss: 0.0207 - val_mae: 0.0583\n",
      "Epoch 370/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0263 - mae: 0.0632 - val_loss: 0.0204 - val_mae: 0.0579\n",
      "Epoch 371/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0271 - mae: 0.0634 - val_loss: 0.0235 - val_mae: 0.0595\n",
      "Epoch 372/500\n",
      "25/25 [==============================] - 0s 1ms/step - loss: 0.0292 - mae: 0.0694 - val_loss: 0.0227 - val_mae: 0.0595\n",
      "Epoch 373/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0270 - mae: 0.0644 - val_loss: 0.0218 - val_mae: 0.0588\n",
      "Epoch 374/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0257 - mae: 0.0631 - val_loss: 0.0231 - val_mae: 0.0588\n",
      "Epoch 375/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0246 - mae: 0.0620 - val_loss: 0.0211 - val_mae: 0.0587\n",
      "Epoch 376/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0242 - mae: 0.0619 - val_loss: 0.0214 - val_mae: 0.0580\n",
      "Epoch 377/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0261 - mae: 0.0642 - val_loss: 0.0202 - val_mae: 0.0566\n",
      "Epoch 378/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0239 - mae: 0.0603 - val_loss: 0.0242 - val_mae: 0.0602\n",
      "Epoch 379/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0283 - mae: 0.0677 - val_loss: 0.0211 - val_mae: 0.0571\n",
      "Epoch 380/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0300 - mae: 0.0669 - val_loss: 0.0212 - val_mae: 0.0566\n",
      "Epoch 381/500\n",
      "25/25 [==============================] - 0s 1ms/step - loss: 0.0260 - mae: 0.0621 - val_loss: 0.0216 - val_mae: 0.0581\n",
      "Epoch 382/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0254 - mae: 0.0620 - val_loss: 0.0214 - val_mae: 0.0576\n",
      "Epoch 383/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0259 - mae: 0.0622 - val_loss: 0.0228 - val_mae: 0.0588\n",
      "Epoch 384/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0309 - mae: 0.0715 - val_loss: 0.0216 - val_mae: 0.0576\n",
      "Epoch 385/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0273 - mae: 0.0669 - val_loss: 0.0199 - val_mae: 0.0562\n",
      "Epoch 386/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0279 - mae: 0.0646 - val_loss: 0.0212 - val_mae: 0.0581\n",
      "Epoch 387/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0308 - mae: 0.0702 - val_loss: 0.0203 - val_mae: 0.0563\n",
      "Epoch 388/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0275 - mae: 0.0654 - val_loss: 0.0212 - val_mae: 0.0575\n",
      "Epoch 389/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0256 - mae: 0.0624 - val_loss: 0.0204 - val_mae: 0.0566\n",
      "Epoch 390/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0274 - mae: 0.0636 - val_loss: 0.0212 - val_mae: 0.0575\n",
      "Epoch 391/500\n",
      "25/25 [==============================] - 0s 1ms/step - loss: 0.0231 - mae: 0.0599 - val_loss: 0.0220 - val_mae: 0.0570\n",
      "Epoch 392/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0238 - mae: 0.0582 - val_loss: 0.0218 - val_mae: 0.0580\n",
      "Epoch 393/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0258 - mae: 0.0618 - val_loss: 0.0207 - val_mae: 0.0574\n",
      "Epoch 394/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0280 - mae: 0.0642 - val_loss: 0.0204 - val_mae: 0.0567\n",
      "Epoch 395/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0261 - mae: 0.0637 - val_loss: 0.0215 - val_mae: 0.0579\n",
      "Epoch 396/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0285 - mae: 0.0686 - val_loss: 0.0232 - val_mae: 0.0580\n",
      "Epoch 397/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0218 - mae: 0.0558 - val_loss: 0.0211 - val_mae: 0.0577\n",
      "Epoch 398/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0211 - mae: 0.0553 - val_loss: 0.0204 - val_mae: 0.0560\n",
      "Epoch 399/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0237 - mae: 0.0569 - val_loss: 0.0217 - val_mae: 0.0569\n",
      "Epoch 400/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0269 - mae: 0.0622 - val_loss: 0.0198 - val_mae: 0.0560\n",
      "Epoch 401/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0280 - mae: 0.0660 - val_loss: 0.0204 - val_mae: 0.0567\n",
      "Epoch 402/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0245 - mae: 0.0613 - val_loss: 0.0242 - val_mae: 0.0588\n",
      "Epoch 403/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0269 - mae: 0.0628 - val_loss: 0.0237 - val_mae: 0.0578\n",
      "Epoch 404/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0271 - mae: 0.0645 - val_loss: 0.0216 - val_mae: 0.0568\n",
      "Epoch 405/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0240 - mae: 0.0578 - val_loss: 0.0210 - val_mae: 0.0566\n",
      "Epoch 406/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0235 - mae: 0.0572 - val_loss: 0.0222 - val_mae: 0.0567\n",
      "Epoch 407/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0263 - mae: 0.0628 - val_loss: 0.0208 - val_mae: 0.0571\n",
      "Epoch 408/500\n",
      "25/25 [==============================] - 0s 1ms/step - loss: 0.0271 - mae: 0.0640 - val_loss: 0.0218 - val_mae: 0.0573\n",
      "Epoch 409/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0264 - mae: 0.0630 - val_loss: 0.0207 - val_mae: 0.0560\n",
      "Epoch 410/500\n",
      "25/25 [==============================] - 0s 1ms/step - loss: 0.0260 - mae: 0.0610 - val_loss: 0.0224 - val_mae: 0.0572\n",
      "Epoch 411/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0267 - mae: 0.0642 - val_loss: 0.0205 - val_mae: 0.0557\n",
      "Epoch 412/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0262 - mae: 0.0625 - val_loss: 0.0218 - val_mae: 0.0563\n",
      "Epoch 413/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0223 - mae: 0.0547 - val_loss: 0.0198 - val_mae: 0.0547\n",
      "Epoch 414/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0234 - mae: 0.0580 - val_loss: 0.0210 - val_mae: 0.0563\n",
      "Epoch 415/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0274 - mae: 0.0647 - val_loss: 0.0231 - val_mae: 0.0569\n",
      "Epoch 416/500\n",
      "25/25 [==============================] - 0s 1ms/step - loss: 0.0246 - mae: 0.0600 - val_loss: 0.0231 - val_mae: 0.0576\n",
      "Epoch 417/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0240 - mae: 0.0588 - val_loss: 0.0202 - val_mae: 0.0553\n",
      "Epoch 418/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0291 - mae: 0.0670 - val_loss: 0.0203 - val_mae: 0.0551\n",
      "Epoch 419/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0251 - mae: 0.0611 - val_loss: 0.0211 - val_mae: 0.0561\n",
      "Epoch 420/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0228 - mae: 0.0559 - val_loss: 0.0232 - val_mae: 0.0573\n",
      "Epoch 421/500\n",
      "25/25 [==============================] - 0s 1ms/step - loss: 0.0285 - mae: 0.0663 - val_loss: 0.0202 - val_mae: 0.0557\n",
      "Epoch 422/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0262 - mae: 0.0601 - val_loss: 0.0210 - val_mae: 0.0557\n",
      "Epoch 423/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0281 - mae: 0.0644 - val_loss: 0.0224 - val_mae: 0.0562\n",
      "Epoch 424/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0238 - mae: 0.0580 - val_loss: 0.0201 - val_mae: 0.0543\n",
      "Epoch 425/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0235 - mae: 0.0565 - val_loss: 0.0224 - val_mae: 0.0569\n",
      "Epoch 426/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0270 - mae: 0.0626 - val_loss: 0.0195 - val_mae: 0.0541\n",
      "Epoch 427/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0243 - mae: 0.0597 - val_loss: 0.0216 - val_mae: 0.0571\n",
      "Epoch 428/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0249 - mae: 0.0599 - val_loss: 0.0212 - val_mae: 0.0549\n",
      "Epoch 429/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0258 - mae: 0.0593 - val_loss: 0.0203 - val_mae: 0.0548\n",
      "Epoch 430/500\n",
      "25/25 [==============================] - 0s 1ms/step - loss: 0.0298 - mae: 0.0653 - val_loss: 0.0201 - val_mae: 0.0552\n",
      "Epoch 431/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0263 - mae: 0.0620 - val_loss: 0.0221 - val_mae: 0.0562\n",
      "Epoch 432/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0263 - mae: 0.0619 - val_loss: 0.0215 - val_mae: 0.0554\n",
      "Epoch 433/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0241 - mae: 0.0570 - val_loss: 0.0221 - val_mae: 0.0554\n",
      "Epoch 434/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0252 - mae: 0.0589 - val_loss: 0.0208 - val_mae: 0.0562\n",
      "Epoch 435/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0294 - mae: 0.0628 - val_loss: 0.0201 - val_mae: 0.0542\n",
      "Epoch 436/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0291 - mae: 0.0664 - val_loss: 0.0200 - val_mae: 0.0550\n",
      "Epoch 437/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0288 - mae: 0.0652 - val_loss: 0.0207 - val_mae: 0.0550\n",
      "Epoch 438/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0239 - mae: 0.0588 - val_loss: 0.0201 - val_mae: 0.0543\n",
      "Epoch 439/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0225 - mae: 0.0556 - val_loss: 0.0212 - val_mae: 0.0551\n",
      "Epoch 440/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0267 - mae: 0.0617 - val_loss: 0.0204 - val_mae: 0.0537\n",
      "Epoch 441/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0238 - mae: 0.0574 - val_loss: 0.0212 - val_mae: 0.0558\n",
      "Epoch 442/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0303 - mae: 0.0689 - val_loss: 0.0201 - val_mae: 0.0534\n",
      "Epoch 443/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0249 - mae: 0.0578 - val_loss: 0.0198 - val_mae: 0.0543\n",
      "Epoch 444/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0245 - mae: 0.0562 - val_loss: 0.0206 - val_mae: 0.0545\n",
      "Epoch 445/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0229 - mae: 0.0563 - val_loss: 0.0207 - val_mae: 0.0540\n",
      "Epoch 446/500\n",
      "25/25 [==============================] - 0s 1ms/step - loss: 0.0285 - mae: 0.0635 - val_loss: 0.0193 - val_mae: 0.0530\n",
      "Epoch 447/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0236 - mae: 0.0550 - val_loss: 0.0220 - val_mae: 0.0559\n",
      "Epoch 448/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0253 - mae: 0.0608 - val_loss: 0.0229 - val_mae: 0.0558\n",
      "Epoch 449/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0250 - mae: 0.0586 - val_loss: 0.0216 - val_mae: 0.0540\n",
      "Epoch 450/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0275 - mae: 0.0633 - val_loss: 0.0204 - val_mae: 0.0539\n",
      "Epoch 451/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0230 - mae: 0.0535 - val_loss: 0.0209 - val_mae: 0.0555\n",
      "Epoch 452/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0247 - mae: 0.0588 - val_loss: 0.0220 - val_mae: 0.0536\n",
      "Epoch 453/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0271 - mae: 0.0604 - val_loss: 0.0206 - val_mae: 0.0544\n",
      "Epoch 454/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0274 - mae: 0.0610 - val_loss: 0.0197 - val_mae: 0.0538\n",
      "Epoch 455/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0293 - mae: 0.0648 - val_loss: 0.0201 - val_mae: 0.0536\n",
      "Epoch 456/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0281 - mae: 0.0630 - val_loss: 0.0217 - val_mae: 0.0552\n",
      "Epoch 457/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0270 - mae: 0.0622 - val_loss: 0.0206 - val_mae: 0.0536\n",
      "Epoch 458/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0295 - mae: 0.0669 - val_loss: 0.0194 - val_mae: 0.0531\n",
      "Epoch 459/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0265 - mae: 0.0601 - val_loss: 0.0229 - val_mae: 0.0550\n",
      "Epoch 460/500\n",
      "25/25 [==============================] - 0s 1ms/step - loss: 0.0282 - mae: 0.0624 - val_loss: 0.0217 - val_mae: 0.0541\n",
      "Epoch 461/500\n",
      "25/25 [==============================] - 0s 1ms/step - loss: 0.0232 - mae: 0.0547 - val_loss: 0.0219 - val_mae: 0.0546\n",
      "Epoch 462/500\n",
      "25/25 [==============================] - 0s 1ms/step - loss: 0.0249 - mae: 0.0589 - val_loss: 0.0203 - val_mae: 0.0534\n",
      "Epoch 463/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0270 - mae: 0.0607 - val_loss: 0.0229 - val_mae: 0.0558\n",
      "Epoch 464/500\n",
      "25/25 [==============================] - 0s 1ms/step - loss: 0.0277 - mae: 0.0636 - val_loss: 0.0209 - val_mae: 0.0533\n",
      "Epoch 465/500\n",
      "25/25 [==============================] - 0s 1ms/step - loss: 0.0282 - mae: 0.0613 - val_loss: 0.0214 - val_mae: 0.0547\n",
      "Epoch 466/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0282 - mae: 0.0636 - val_loss: 0.0195 - val_mae: 0.0526\n",
      "Epoch 467/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0253 - mae: 0.0594 - val_loss: 0.0211 - val_mae: 0.0548\n",
      "Epoch 468/500\n",
      "25/25 [==============================] - 0s 1ms/step - loss: 0.0300 - mae: 0.0664 - val_loss: 0.0204 - val_mae: 0.0544\n",
      "Epoch 469/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0244 - mae: 0.0568 - val_loss: 0.0198 - val_mae: 0.0533\n",
      "Epoch 470/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0235 - mae: 0.0568 - val_loss: 0.0201 - val_mae: 0.0533\n",
      "Epoch 471/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0236 - mae: 0.0562 - val_loss: 0.0193 - val_mae: 0.0520\n",
      "Epoch 472/500\n",
      "25/25 [==============================] - 0s 1ms/step - loss: 0.0245 - mae: 0.0604 - val_loss: 0.0247 - val_mae: 0.0565\n",
      "Epoch 473/500\n",
      "25/25 [==============================] - 0s 1ms/step - loss: 0.0253 - mae: 0.0566 - val_loss: 0.0194 - val_mae: 0.0525\n",
      "Epoch 474/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0297 - mae: 0.0658 - val_loss: 0.0209 - val_mae: 0.0543\n",
      "Epoch 475/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0246 - mae: 0.0577 - val_loss: 0.0196 - val_mae: 0.0521\n",
      "Epoch 476/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0266 - mae: 0.0599 - val_loss: 0.0196 - val_mae: 0.0527\n",
      "Epoch 477/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0247 - mae: 0.0570 - val_loss: 0.0231 - val_mae: 0.0549\n",
      "Epoch 478/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0300 - mae: 0.0657 - val_loss: 0.0208 - val_mae: 0.0533\n",
      "Epoch 479/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0246 - mae: 0.0578 - val_loss: 0.0204 - val_mae: 0.0529\n",
      "Epoch 480/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0225 - mae: 0.0534 - val_loss: 0.0207 - val_mae: 0.0531\n",
      "Epoch 481/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0256 - mae: 0.0589 - val_loss: 0.0215 - val_mae: 0.0536\n",
      "Epoch 482/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0279 - mae: 0.0612 - val_loss: 0.0200 - val_mae: 0.0534\n",
      "Epoch 483/500\n",
      "25/25 [==============================] - 0s 1ms/step - loss: 0.0295 - mae: 0.0655 - val_loss: 0.0192 - val_mae: 0.0516\n",
      "Epoch 484/500\n",
      "25/25 [==============================] - 0s 1ms/step - loss: 0.0259 - mae: 0.0590 - val_loss: 0.0202 - val_mae: 0.0532\n",
      "Epoch 485/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0271 - mae: 0.0610 - val_loss: 0.0224 - val_mae: 0.0539\n",
      "Epoch 486/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0281 - mae: 0.0618 - val_loss: 0.0213 - val_mae: 0.0540\n",
      "Epoch 487/500\n",
      "25/25 [==============================] - 0s 1ms/step - loss: 0.0242 - mae: 0.0575 - val_loss: 0.0192 - val_mae: 0.0520\n",
      "Epoch 488/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0206 - mae: 0.0516 - val_loss: 0.0209 - val_mae: 0.0533\n",
      "Epoch 489/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0303 - mae: 0.0668 - val_loss: 0.0191 - val_mae: 0.0517\n",
      "Epoch 490/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0264 - mae: 0.0604 - val_loss: 0.0216 - val_mae: 0.0543\n",
      "Epoch 491/500\n",
      "25/25 [==============================] - 0s 1ms/step - loss: 0.0287 - mae: 0.0642 - val_loss: 0.0224 - val_mae: 0.0536\n",
      "Epoch 492/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0308 - mae: 0.0658 - val_loss: 0.0205 - val_mae: 0.0528\n",
      "Epoch 493/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0250 - mae: 0.0587 - val_loss: 0.0207 - val_mae: 0.0531\n",
      "Epoch 494/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0262 - mae: 0.0615 - val_loss: 0.0220 - val_mae: 0.0533\n",
      "Epoch 495/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0291 - mae: 0.0641 - val_loss: 0.0224 - val_mae: 0.0537\n",
      "Epoch 496/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0257 - mae: 0.0600 - val_loss: 0.0214 - val_mae: 0.0531\n",
      "Epoch 497/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0258 - mae: 0.0611 - val_loss: 0.0200 - val_mae: 0.0525\n",
      "Epoch 498/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0231 - mae: 0.0563 - val_loss: 0.0193 - val_mae: 0.0519\n",
      "Epoch 499/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0262 - mae: 0.0591 - val_loss: 0.0201 - val_mae: 0.0523\n",
      "Epoch 500/500\n",
      "25/25 [==============================] - 0s 2ms/step - loss: 0.0273 - mae: 0.0605 - val_loss: 0.0202 - val_mae: 0.0524\n"
     ]
    }
   ],
   "source": [
    "#Train the model on our training data while validating on our validation set\n",
    "history = model.fit(train_x, train_y, epochs=500, batch_size=64,\n",
    "                        validation_data=(x_validate, y_validate))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Draw a graph of the loss, which is the distance between\n",
    "# the predicted and actual values during training and validation.\n",
    "loss = history.history['loss']\n",
    "val_loss = history.history['val_loss']\n",
    "\n",
    "epochs = range(1, len(loss) + 1)\n",
    "\n",
    "plt.plot(epochs, loss, 'g.', label='Training loss')\n",
    "plt.plot(epochs, val_loss, 'b', label='Validation loss')\n",
    "plt.title('Training and validation loss')\n",
    "plt.xlabel('Epochs')\n",
    "plt.ylabel('Loss')\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n",
    "# Exclude the first few epochs so the graph is easier to read\n",
    "SKIP = 100\n",
    "\n",
    "plt.clf()\n",
    "\n",
    "plt.plot(epochs[SKIP:], loss[SKIP:], 'g.', label='Training loss')\n",
    "plt.plot(epochs[SKIP:], val_loss[SKIP:], 'b.', label='Validation loss')\n",
    "plt.title('Training and validation loss')\n",
    "plt.xlabel('Epochs')\n",
    "plt.ylabel('Loss')\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n",
    "plt.clf()\n",
    "\n",
    "# Draw a graph of mean absolute error, which is another way of\n",
    "# measuring the amount of error in the prediction.\n",
    "mae = history.history['mae']\n",
    "val_mae = history.history['val_mae']\n",
    "\n",
    "plt.plot(epochs[SKIP:], mae[SKIP:], 'g.', label='Training MAE')\n",
    "plt.plot(epochs[SKIP:], val_mae[SKIP:], 'b.', label='Validation MAE')\n",
    "plt.title('Training and validation mean absolute error')\n",
    "plt.xlabel('Epochs')\n",
    "plt.ylabel('MAE')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_data(test_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "test_label: {'sin': 0, 'cos': 1}\n",
      "label_num: [56, 44]\n",
      "4/4 [==============================] - 0s 679us/step - loss: 0.0269 - mae: 0.0611\n",
      "Test loss: 0.026900094002485275\n",
      "Test MAE: 0.06110972911119461\n"
     ]
    }
   ],
   "source": [
    "test_label = {'sin': 0, 'cos': 1}\n",
    "test_label_num = [0] * len(test_label)\n",
    "\n",
    "for _ in test_data:\n",
    "    curve_type = _['curve']\n",
    "    label_num = test_label[curve_type]\n",
    "    test_label_num[label_num] += 1\n",
    "\n",
    "print(\"test_label:\", test_label)\n",
    "print(\"label_num:\", test_label_num)\n",
    "\n",
    "# Calculate and print the loss on our test dataset\n",
    "test_loss, test_mae = model.evaluate(x_test, y_test)\n",
    "\n",
    "print('Test loss:', test_loss)\n",
    "print('Test MAE:', test_mae)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "if not os.path.exists(\"./model\"):\n",
    "    os.mkdir(\"./model\")\n",
    "\n",
    "model.save(\"./model/model.h5\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input_1 (InputLayer)         [(None, 2)]               0         \n",
      "_________________________________________________________________\n",
      "relu (Dense)                 (None, 16)                48        \n",
      "_________________________________________________________________\n",
      "relu2 (Dense)                (None, 16)                272       \n",
      "_________________________________________________________________\n",
      "output (Dense)               (None, 1)                 17        \n",
      "=================================================================\n",
      "Total params: 337\n",
      "Trainable params: 337\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "Weights have been saved to model.txt\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "import numpy as np\n",
    "import os\n",
    "\n",
    "# 确认文件是否存在\n",
    "model_path = './model/model.h5'\n",
    "if not os.path.exists(model_path):\n",
    "    raise FileNotFoundError(f\"Model file not found at: {model_path}\")\n",
    "\n",
    "# 加载整个模型\n",
    "try:\n",
    "    model = tf.keras.models.load_model(model_path)\n",
    "except OSError as e:\n",
    "    print(f\"Error loading model: {e}\")\n",
    "    raise\n",
    "\n",
    "# 打印模型的结构，检查层名\n",
    "model.summary()\n",
    "\n",
    "# 创建或打开一个文件来保存权重数据\n",
    "with open('./model/model.txt', 'w') as f:\n",
    "    for layer in model.layers:  # 直接遍历 model.layers\n",
    "        # 获取层的名称\n",
    "        layer_name = layer.name  # 正确访问层对象的 name 属性\n",
    "        f.write(f\"Layer: {layer_name}\\n\")\n",
    "        \n",
    "        # 获取层的权重\n",
    "        weights = layer.get_weights()\n",
    "        if weights:\n",
    "            # 如果层有权重，则打印权重的形状和数据\n",
    "            if len(weights) == 2:\n",
    "                W, b = weights\n",
    "                f.write(\" Weight:\\n\")\n",
    "                f.write(f\"  Shape: {W.shape}\\n\")\n",
    "                f.write(f\"  Data: {W}\\n\")\n",
    "\n",
    "                f.write(\" Bias:\\n\")\n",
    "                f.write(f\"  Shape: {b.shape}\\n\")\n",
    "                f.write(f\"  Data: {b}\\n\")\n",
    "            else:\n",
    "                for i, weight in enumerate(weights):\n",
    "                    f.write(f\" Weight {i}:\\n\")\n",
    "                    f.write(f\"  Shape: {weight.shape}\\n\")\n",
    "                    f.write(f\"  Data: {weight}\\n\")\n",
    "        else:\n",
    "            f.write(\"  No weights available\\n\")\n",
    "        f.write(\"\\n\")\n",
    "\n",
    "print(\"Weights have been saved to model.txt\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input_1 (InputLayer)         [(None, 2)]               0         \n",
      "_________________________________________________________________\n",
      "relu (Dense)                 (None, 16)                48        \n",
      "_________________________________________________________________\n",
      "relu2 (Dense)                (None, 16)                272       \n",
      "_________________________________________________________________\n",
      "output (Dense)               (None, 1)                 17        \n",
      "=================================================================\n",
      "Total params: 337\n",
      "Trainable params: 337\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "from tensorflow import *\n",
    "import numpy as np\n",
    "\n",
    "test_model = tf.keras.models.load_model('./model/model.h5')\n",
    "\n",
    "test_model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "def run_model_logo(run_model_data, run_test_label: dict):\n",
    "    run_test_label_num = [0] * len(run_test_label)\n",
    "\n",
    "    for _ in run_model_data:\n",
    "        curve_type = _['curve']\n",
    "        label_num = run_test_label[curve_type]\n",
    "        run_test_label_num[label_num] += 1\n",
    "\n",
    "    print(\"test_label:\", run_test_label)\n",
    "    print(\"label_num:\", run_test_label_num)\n",
    "\n",
    "    run_model_input_y = [item['curve'] for item in run_model_data]\n",
    "    run_model_input_x = np.array([[item['x'], item['y']] for item in run_model_data])\n",
    "    \n",
    "    print(\"run model input x:\")\n",
    "    print('-' * 40)\n",
    "    print(run_model_input_x)\n",
    "\n",
    "    return run_model_input_x, run_model_input_y, run_test_label_num\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "test_label: {'cos': 1, 'sin': 0}\n",
      "label_num: [56, 44]\n",
      "run model input x:\n",
      "----------------------------------------\n",
      "array([[ 1.46544762,  0.16031256],\n",
      "       [ 2.73592153,  0.43530235],\n",
      "       [ 5.00013245,  0.40346429],\n",
      "       [ 4.55357974, -0.10050192],\n",
      "       [ 5.94984314, -0.41071394],\n",
      "       [ 0.99373702,  0.61322159],\n",
      "       [ 3.35229006, -0.39958164],\n",
      "       [ 3.54726378, -0.46107173],\n",
      "       [ 3.76110592, -0.50749654],\n",
      "       [ 3.12586897, -1.00361073],\n",
      "       [ 5.05044825, -0.83716232],\n",
      "       [ 4.07557966, -0.62876886],\n",
      "       [ 2.94976367, -0.7220735 ],\n",
      "       [ 1.65413187, -0.13066137],\n",
      "       [ 3.45292166, -0.99407059],\n",
      "       [ 5.7863168 ,  0.80471307],\n",
      "       [ 4.32086918, -0.6820786 ],\n",
      "       [ 3.86802699, -0.53118068],\n",
      "       [ 6.08821159, -0.04237077],\n",
      "       [ 4.88063243,  0.31983793],\n",
      "       [ 0.03144737,  0.98886275],\n",
      "       [ 5.57247466, -0.80783206],\n",
      "       [ 0.40881586,  0.41361457],\n",
      "       [ 5.02529035,  0.30418673],\n",
      "       [ 3.88689542, -0.75944674],\n",
      "       [ 0.03773685,  0.10895041],\n",
      "       [ 4.42150077, -0.88442475],\n",
      "       [ 2.52836886, -0.83524406],\n",
      "       [ 2.2956583 , -0.70213026],\n",
      "       [ 1.80507926,  1.01115978],\n",
      "       [ 4.84289558, -1.10842752],\n",
      "       [ 0.77989487,  0.73743618],\n",
      "       [ 4.83660611,  0.05070972],\n",
      "       [ 3.53468483, -0.36704433],\n",
      "       [ 0.30189479,  0.81051862],\n",
      "       [ 2.42144779,  0.75560743],\n",
      "       [ 2.97492157, -0.95823717],\n",
      "       [ 2.15728985,  0.79396373],\n",
      "       [ 0.22642109,  0.1365694 ],\n",
      "       [ 2.66673731, -0.85710275],\n",
      "       [ 1.27047391,  0.91634619],\n",
      "       [ 0.17610529,  1.15947402],\n",
      "       [ 0.54089483,  0.3786734 ],\n",
      "       [ 5.09447457, -0.9165532 ],\n",
      "       [ 3.65418485, -0.35247409],\n",
      "       [ 0.71071065,  0.73367089],\n",
      "       [ 5.61650098,  0.7085194 ],\n",
      "       [ 4.30200075, -0.98921412],\n",
      "       [ 0.7736054 ,  0.72171485],\n",
      "       [ 4.06929018, -0.78930795],\n",
      "       [ 5.94355367, -0.38848045],\n",
      "       [ 0.59121063,  1.11447048],\n",
      "       [ 4.52213237, -1.01173484],\n",
      "       [ 6.14481686, -0.11431666],\n",
      "       [ 4.76742188,  0.05851876],\n",
      "       [ 5.39636936, -0.84697998],\n",
      "       [ 1.15726336,  1.01126993],\n",
      "       [ 5.13221142,  0.2817713 ],\n",
      "       [ 3.23907951, -0.22760813],\n",
      "       [ 0.8490791 ,  0.84316885],\n",
      "       [ 1.58494765,  0.07930885],\n",
      "       [ 4.32715865, -0.96715045],\n",
      "       [ 5.75486943,  0.80286145],\n",
      "       [ 2.57239519,  0.59905821],\n",
      "       [ 2.9183163 ,  0.11255202],\n",
      "       [ 1.90571086,  1.1114192 ],\n",
      "       [ 2.44660569,  0.53355402],\n",
      "       [ 0.28302637,  0.38039485],\n",
      "       [ 2.0943951 ,  0.79772943],\n",
      "       [ 5.15107985, -1.0084846 ],\n",
      "       [ 4.54729027, -0.18689136],\n",
      "       [ 1.91200033, -0.34767351],\n",
      "       [ 3.86173752, -0.49801916],\n",
      "       [ 5.1888167 ,  0.51890403],\n",
      "       [ 2.8680005 ,  0.28034142],\n",
      "       [ 1.67300029,  0.98247308],\n",
      "       [ 2.0440793 , -0.51370656],\n",
      "       [ 0.78618435,  0.61806744],\n",
      "       [ 4.82402716, -0.92747116],\n",
      "       [ 3.03781632, -0.06305213],\n",
      "       [ 3.82400067, -0.80931515],\n",
      "       [ 2.27678987, -0.61078447],\n",
      "       [ 4.15105336, -0.79948455],\n",
      "       [ 5.15736932, -0.82250518],\n",
      "       [ 2.46547411, -0.80233562],\n",
      "       [ 2.51578991, -0.81036663],\n",
      "       [ 5.37750094,  0.51675278],\n",
      "       [ 4.9875535 ,  0.28321546],\n",
      "       [ 0.71071065,  0.72978729],\n",
      "       [ 6.00015894, -0.14864427],\n",
      "       [ 2.72963206,  0.46373129],\n",
      "       [ 3.17618476, -0.03072847],\n",
      "       [ 1.35223708,  0.23601133],\n",
      "       [ 1.50318447,  0.20449568],\n",
      "       [ 2.50321096,  0.57141644],\n",
      "       [ 3.05668474, -0.89597362],\n",
      "       [ 0.05660527,  0.05237984],\n",
      "       [ 0.29560531,  1.02991259],\n",
      "       [ 3.93721121, -0.5295586 ],\n",
      "       [ 5.66681678, -0.57987648]])\n"
     ]
    }
   ],
   "source": [
    "run_test_data = json.load(open('./data/test.txt', 'r'))\n",
    "run_test_label = {'sin': 0, 'cos': 1}\n",
    "run_model_input_x, run_model_input_y, run_test_label_num = run_model_logo(run_test_data, run_test_label)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Input x: [[1.46544762 0.16031256]]\n",
      "Predicted y: [[0.99156445]]\n",
      "Original label: cos\n",
      "Predict label: cos\n",
      "Correct prediction\n",
      "\n",
      "Input x: [[2.73592153 0.43530235]]\n",
      "Predicted y: [[0.00093916]]\n",
      "Original label: sin\n",
      "Predict label: sin\n",
      "Correct prediction\n",
      "\n",
      "Input x: [[5.00013245 0.40346429]]\n",
      "Predicted y: [[0.99981856]]\n",
      "Original label: cos\n",
      "Predict label: cos\n",
      "Correct prediction\n",
      "\n",
      "Input x: [[ 4.55357974 -0.10050192]]\n",
      "Predicted y: [[0.99571604]]\n",
      "Original label: cos\n",
      "Predict label: cos\n",
      "Correct prediction\n",
      "\n",
      "Input x: [[ 5.94984314 -0.41071394]]\n",
      "Predicted y: [[8.2745396e-07]]\n",
      "Original label: sin\n",
      "Predict label: sin\n",
      "Correct prediction\n",
      "\n",
      "Input x: [[0.99373702 0.61322159]]\n",
      "Predicted y: [[0.8365997]]\n",
      "Original label: cos\n",
      "Predict label: sin\n",
      "Incorrect predicti\n",
      "\n",
      "Input x: [[ 3.35229006 -0.39958164]]\n",
      "Predicted y: [[0.05766153]]\n",
      "Original label: sin\n",
      "Predict label: sin\n",
      "Correct prediction\n",
      "\n",
      "Input x: [[ 3.54726378 -0.46107173]]\n",
      "Predicted y: [[0.03654128]]\n",
      "Original label: sin\n",
      "Predict label: sin\n",
      "Correct prediction\n",
      "\n",
      "Input x: [[ 3.76110592 -0.50749654]]\n",
      "Predicted y: [[0.14058596]]\n",
      "Original label: sin\n",
      "Predict label: sin\n",
      "Correct prediction\n",
      "\n",
      "Input x: [[ 3.12586897 -1.00361073]]\n",
      "Predicted y: [[0.99970084]]\n",
      "Original label: cos\n",
      "Predict label: cos\n",
      "Correct prediction\n",
      "\n",
      "Input x: [[ 5.05044825 -0.83716232]]\n",
      "Predicted y: [[3.531093e-07]]\n",
      "Original label: sin\n",
      "Predict label: sin\n",
      "Correct prediction\n",
      "\n",
      "Input x: [[ 4.07557966 -0.62876886]]\n",
      "Predicted y: [[0.7183617]]\n",
      "Original label: cos\n",
      "Predict label: sin\n",
      "Incorrect predicti\n",
      "\n",
      "Input x: [[ 2.94976367 -0.7220735 ]]\n",
      "Predicted y: [[0.9978714]]\n",
      "Original label: cos\n",
      "Predict label: cos\n",
      "Correct prediction\n",
      "\n",
      "Input x: [[ 1.65413187 -0.13066137]]\n",
      "Predicted y: [[0.9964098]]\n",
      "Original label: cos\n",
      "Predict label: cos\n",
      "Correct prediction\n",
      "\n",
      "Input x: [[ 3.45292166 -0.99407059]]\n",
      "Predicted y: [[0.99858165]]\n",
      "Original label: cos\n",
      "Predict label: cos\n",
      "Correct prediction\n",
      "\n",
      "Input x: [[5.7863168  0.80471307]]\n",
      "Predicted y: [[1.]]\n",
      "Original label: cos\n",
      "Predict label: cos\n",
      "Correct prediction\n",
      "\n",
      "Input x: [[ 4.32086918 -0.6820786 ]]\n",
      "Predicted y: [[0.16624385]]\n",
      "Original label: sin\n",
      "Predict label: sin\n",
      "Correct prediction\n",
      "\n",
      "Input x: [[ 3.86802699 -0.53118068]]\n",
      "Predicted y: [[0.36207214]]\n",
      "Original label: sin\n",
      "Predict label: sin\n",
      "Correct prediction\n",
      "\n",
      "Input x: [[ 6.08821159 -0.04237077]]\n",
      "Predicted y: [[0.00328496]]\n",
      "Original label: sin\n",
      "Predict label: sin\n",
      "Correct prediction\n",
      "\n",
      "Input x: [[4.88063243 0.31983793]]\n",
      "Predicted y: [[0.99949706]]\n",
      "Original label: cos\n",
      "Predict label: cos\n",
      "Correct prediction\n",
      "\n",
      "Input x: [[0.03144737 0.98886275]]\n",
      "Predicted y: [[0.9986489]]\n",
      "Original label: cos\n",
      "Predict label: cos\n",
      "Correct prediction\n",
      "\n",
      "Input x: [[ 5.57247466 -0.80783206]]\n",
      "Predicted y: [[2.8098894e-09]]\n",
      "Original label: sin\n",
      "Predict label: sin\n",
      "Correct prediction\n",
      "\n",
      "Input x: [[0.40881586 0.41361457]]\n",
      "Predicted y: [[0.00540861]]\n",
      "Original label: sin\n",
      "Predict label: sin\n",
      "Correct prediction\n",
      "\n",
      "Input x: [[5.02529035 0.30418673]]\n",
      "Predicted y: [[0.9999012]]\n",
      "Original label: cos\n",
      "Predict label: cos\n",
      "Correct prediction\n",
      "\n",
      "Input x: [[ 3.88689542 -0.75944674]]\n",
      "Predicted y: [[0.50772893]]\n",
      "Original label: cos\n",
      "Predict label: sin\n",
      "Incorrect predicti\n",
      "\n",
      "Input x: [[0.03773685 0.10895041]]\n",
      "Predicted y: [[2.2653976e-05]]\n",
      "Original label: sin\n",
      "Predict label: sin\n",
      "Correct prediction\n",
      "\n",
      "Input x: [[ 4.42150077 -0.88442475]]\n",
      "Predicted y: [[0.00052363]]\n",
      "Original label: sin\n",
      "Predict label: sin\n",
      "Correct prediction\n",
      "\n",
      "Input x: [[ 2.52836886 -0.83524406]]\n",
      "Predicted y: [[0.99969244]]\n",
      "Original label: cos\n",
      "Predict label: cos\n",
      "Correct prediction\n",
      "\n",
      "Input x: [[ 2.2956583  -0.70213026]]\n",
      "Predicted y: [[0.9995285]]\n",
      "Original label: cos\n",
      "Predict label: cos\n",
      "Correct prediction\n",
      "\n",
      "Input x: [[1.80507926 1.01115978]]\n",
      "Predicted y: [[0.00028229]]\n",
      "Original label: sin\n",
      "Predict label: sin\n",
      "Correct prediction\n",
      "\n",
      "Input x: [[ 4.84289558 -1.10842752]]\n",
      "Predicted y: [[1.8230195e-06]]\n",
      "Original label: sin\n",
      "Predict label: sin\n",
      "Correct prediction\n",
      "\n",
      "Input x: [[0.77989487 0.73743618]]\n",
      "Predicted y: [[0.7351915]]\n",
      "Original label: cos\n",
      "Predict label: sin\n",
      "Incorrect predicti\n",
      "\n",
      "Input x: [[4.83660611 0.05070972]]\n",
      "Predicted y: [[0.99967176]]\n",
      "Original label: cos\n",
      "Predict label: cos\n",
      "Correct prediction\n",
      "\n",
      "Input x: [[ 3.53468483 -0.36704433]]\n",
      "Predicted y: [[0.01779601]]\n",
      "Original label: sin\n",
      "Predict label: sin\n",
      "Correct prediction\n",
      "\n",
      "Input x: [[0.30189479 0.81051862]]\n",
      "Predicted y: [[0.9859072]]\n",
      "Original label: cos\n",
      "Predict label: cos\n",
      "Correct prediction\n",
      "\n",
      "Input x: [[2.42144779 0.75560743]]\n",
      "Predicted y: [[0.00028241]]\n",
      "Original label: sin\n",
      "Predict label: sin\n",
      "Correct prediction\n",
      "\n",
      "Input x: [[ 2.97492157 -0.95823717]]\n",
      "Predicted y: [[0.99974704]]\n",
      "Original label: cos\n",
      "Predict label: cos\n",
      "Correct prediction\n",
      "\n",
      "Input x: [[2.15728985 0.79396373]]\n",
      "Predicted y: [[0.00073114]]\n",
      "Original label: sin\n",
      "Predict label: sin\n",
      "Correct prediction\n",
      "\n",
      "Input x: [[0.22642109 0.1365694 ]]\n",
      "Predicted y: [[0.00016367]]\n",
      "Original label: sin\n",
      "Predict label: sin\n",
      "Correct prediction\n",
      "\n",
      "Input x: [[ 2.66673731 -0.85710275]]\n",
      "Predicted y: [[0.99968743]]\n",
      "Original label: cos\n",
      "Predict label: cos\n",
      "Correct prediction\n",
      "\n",
      "Input x: [[1.27047391 0.91634619]]\n",
      "Predicted y: [[0.00290033]]\n",
      "Original label: sin\n",
      "Predict label: sin\n",
      "Correct prediction\n",
      "\n",
      "Input x: [[0.17610529 1.15947402]]\n",
      "Predicted y: [[0.99992895]]\n",
      "Original label: cos\n",
      "Predict label: cos\n",
      "Correct prediction\n",
      "\n",
      "Input x: [[0.54089483 0.3786734 ]]\n",
      "Predicted y: [[0.00879627]]\n",
      "Original label: sin\n",
      "Predict label: sin\n",
      "Correct prediction\n",
      "\n",
      "Input x: [[ 5.09447457 -0.9165532 ]]\n",
      "Predicted y: [[9.614854e-08]]\n",
      "Original label: sin\n",
      "Predict label: sin\n",
      "Correct prediction\n",
      "\n",
      "Input x: [[ 3.65418485 -0.35247409]]\n",
      "Predicted y: [[0.02940497]]\n",
      "Original label: sin\n",
      "Predict label: sin\n",
      "Correct prediction\n",
      "\n",
      "Input x: [[0.71071065 0.73367089]]\n",
      "Predicted y: [[0.8035953]]\n",
      "Original label: cos\n",
      "Predict label: sin\n",
      "Incorrect predicti\n",
      "\n",
      "Input x: [[5.61650098 0.7085194 ]]\n",
      "Predicted y: [[0.9999994]]\n",
      "Original label: cos\n",
      "Predict label: cos\n",
      "Correct prediction\n",
      "\n",
      "Input x: [[ 4.30200075 -0.98921412]]\n",
      "Predicted y: [[0.00155398]]\n",
      "Original label: sin\n",
      "Predict label: sin\n",
      "Correct prediction\n",
      "\n",
      "Input x: [[0.7736054  0.72171485]]\n",
      "Predicted y: [[0.7321066]]\n",
      "Original label: sin\n",
      "Predict label: sin\n",
      "Correct prediction\n",
      "\n",
      "Input x: [[ 4.06929018 -0.78930795]]\n",
      "Predicted y: [[0.214169]]\n",
      "Original label: sin\n",
      "Predict label: sin\n",
      "Correct prediction\n",
      "\n",
      "Input x: [[ 5.94355367 -0.38848045]]\n",
      "Predicted y: [[1.5957595e-06]]\n",
      "Original label: sin\n",
      "Predict label: sin\n",
      "Correct prediction\n",
      "\n",
      "Input x: [[0.59121063 1.11447048]]\n",
      "Predicted y: [[0.9270203]]\n",
      "Original label: cos\n",
      "Predict label: cos\n",
      "Correct prediction\n",
      "\n",
      "Input x: [[ 4.52213237 -1.01173484]]\n",
      "Predicted y: [[9.078579e-05]]\n",
      "Original label: sin\n",
      "Predict label: sin\n",
      "Correct prediction\n",
      "\n",
      "Input x: [[ 6.14481686 -0.11431666]]\n",
      "Predicted y: [[0.00026047]]\n",
      "Original label: sin\n",
      "Predict label: sin\n",
      "Correct prediction\n",
      "\n",
      "Input x: [[4.76742188 0.05851876]]\n",
      "Predicted y: [[0.99928445]]\n",
      "Original label: cos\n",
      "Predict label: cos\n",
      "Correct prediction\n",
      "\n",
      "Input x: [[ 5.39636936 -0.84697998]]\n",
      "Predicted y: [[7.649443e-09]]\n",
      "Original label: sin\n",
      "Predict label: sin\n",
      "Correct prediction\n",
      "\n",
      "Input x: [[1.15726336 1.01126993]]\n",
      "Predicted y: [[0.00105053]]\n",
      "Original label: sin\n",
      "Predict label: sin\n",
      "Correct prediction\n",
      "\n",
      "Input x: [[5.13221142 0.2817713 ]]\n",
      "Predicted y: [[0.9999714]]\n",
      "Original label: cos\n",
      "Predict label: cos\n",
      "Correct prediction\n",
      "\n",
      "Input x: [[ 3.23907951 -0.22760813]]\n",
      "Predicted y: [[0.0226106]]\n",
      "Original label: sin\n",
      "Predict label: sin\n",
      "Correct prediction\n",
      "\n",
      "Input x: [[0.8490791  0.84316885]]\n",
      "Predicted y: [[0.1060383]]\n",
      "Original label: sin\n",
      "Predict label: sin\n",
      "Correct prediction\n",
      "\n",
      "Input x: [[1.58494765 0.07930885]]\n",
      "Predicted y: [[0.99326307]]\n",
      "Original label: cos\n",
      "Predict label: cos\n",
      "Correct prediction\n",
      "\n",
      "Input x: [[ 4.32715865 -0.96715045]]\n",
      "Predicted y: [[0.00120777]]\n",
      "Original label: sin\n",
      "Predict label: sin\n",
      "Correct prediction\n",
      "\n",
      "Input x: [[5.75486943 0.80286145]]\n",
      "Predicted y: [[0.9999999]]\n",
      "Original label: cos\n",
      "Predict label: cos\n",
      "Correct prediction\n",
      "\n",
      "Input x: [[2.57239519 0.59905821]]\n",
      "Predicted y: [[0.00051489]]\n",
      "Original label: sin\n",
      "Predict label: sin\n",
      "Correct prediction\n",
      "\n",
      "Input x: [[2.9183163  0.11255202]]\n",
      "Predicted y: [[0.00601587]]\n",
      "Original label: sin\n",
      "Predict label: sin\n",
      "Correct prediction\n",
      "\n",
      "Input x: [[1.90571086 1.1114192 ]]\n",
      "Predicted y: [[6.085312e-05]]\n",
      "Original label: sin\n",
      "Predict label: sin\n",
      "Correct prediction\n",
      "\n",
      "Input x: [[2.44660569 0.53355402]]\n",
      "Predicted y: [[0.00164986]]\n",
      "Original label: sin\n",
      "Predict label: sin\n",
      "Correct prediction\n",
      "\n",
      "Input x: [[0.28302637 0.38039485]]\n",
      "Predicted y: [[0.003497]]\n",
      "Original label: sin\n",
      "Predict label: sin\n",
      "Correct prediction\n",
      "\n",
      "Input x: [[2.0943951  0.79772943]]\n",
      "Predicted y: [[0.00095978]]\n",
      "Original label: sin\n",
      "Predict label: sin\n",
      "Correct prediction\n",
      "\n",
      "Input x: [[ 5.15107985 -1.0084846 ]]\n",
      "Predicted y: [[5.097273e-08]]\n",
      "Original label: sin\n",
      "Predict label: sin\n",
      "Correct prediction\n",
      "\n",
      "Input x: [[ 4.54729027 -0.18689136]]\n",
      "Predicted y: [[0.99656737]]\n",
      "Original label: cos\n",
      "Predict label: cos\n",
      "Correct prediction\n",
      "\n",
      "Input x: [[ 1.91200033 -0.34767351]]\n",
      "Predicted y: [[0.9983493]]\n",
      "Original label: cos\n",
      "Predict label: cos\n",
      "Correct prediction\n",
      "\n",
      "Input x: [[ 3.86173752 -0.49801916]]\n",
      "Predicted y: [[0.3215769]]\n",
      "Original label: sin\n",
      "Predict label: sin\n",
      "Correct prediction\n",
      "\n",
      "Input x: [[5.1888167  0.51890403]]\n",
      "Predicted y: [[0.9999656]]\n",
      "Original label: cos\n",
      "Predict label: cos\n",
      "Correct prediction\n",
      "\n",
      "Input x: [[2.8680005  0.28034142]]\n",
      "Predicted y: [[0.00184998]]\n",
      "Original label: sin\n",
      "Predict label: sin\n",
      "Correct prediction\n",
      "\n",
      "Input x: [[1.67300029 0.98247308]]\n",
      "Predicted y: [[0.00053856]]\n",
      "Original label: sin\n",
      "Predict label: sin\n",
      "Correct prediction\n",
      "\n",
      "Input x: [[ 2.0440793  -0.51370656]]\n",
      "Predicted y: [[0.9990777]]\n",
      "Original label: cos\n",
      "Predict label: cos\n",
      "Correct prediction\n",
      "\n",
      "Input x: [[0.78618435 0.61806744]]\n",
      "Predicted y: [[0.44309306]]\n",
      "Original label: cos\n",
      "Predict label: sin\n",
      "Incorrect predicti\n",
      "\n",
      "Input x: [[ 4.82402716 -0.92747116]]\n",
      "Predicted y: [[2.3108794e-06]]\n",
      "Original label: sin\n",
      "Predict label: sin\n",
      "Correct prediction\n",
      "\n",
      "Input x: [[ 3.03781632 -0.06305213]]\n",
      "Predicted y: [[0.01488891]]\n",
      "Original label: sin\n",
      "Predict label: sin\n",
      "Correct prediction\n",
      "\n",
      "Input x: [[ 3.82400067 -0.80931515]]\n",
      "Predicted y: [[0.6755092]]\n",
      "Original label: cos\n",
      "Predict label: sin\n",
      "Incorrect predicti\n",
      "\n",
      "Input x: [[ 2.27678987 -0.61078447]]\n",
      "Predicted y: [[0.99928683]]\n",
      "Original label: cos\n",
      "Predict label: cos\n",
      "Correct prediction\n",
      "\n",
      "Input x: [[ 4.15105336 -0.79948455]]\n",
      "Predicted y: [[0.07105082]]\n",
      "Original label: sin\n",
      "Predict label: sin\n",
      "Correct prediction\n",
      "\n",
      "Input x: [[ 5.15736932 -0.82250518]]\n",
      "Predicted y: [[1.5450554e-07]]\n",
      "Original label: sin\n",
      "Predict label: sin\n",
      "Correct prediction\n",
      "\n",
      "Input x: [[ 2.46547411 -0.80233562]]\n",
      "Predicted y: [[0.9996597]]\n",
      "Original label: cos\n",
      "Predict label: cos\n",
      "Correct prediction\n",
      "\n",
      "Input x: [[ 2.51578991 -0.81036663]]\n",
      "Predicted y: [[0.9996579]]\n",
      "Original label: cos\n",
      "Predict label: cos\n",
      "Correct prediction\n",
      "\n",
      "Input x: [[5.37750094 0.51675278]]\n",
      "Predicted y: [[0.9999957]]\n",
      "Original label: cos\n",
      "Predict label: cos\n",
      "Correct prediction\n",
      "\n",
      "Input x: [[4.9875535  0.28321546]]\n",
      "Predicted y: [[0.9998611]]\n",
      "Original label: cos\n",
      "Predict label: cos\n",
      "Correct prediction\n",
      "\n",
      "Input x: [[0.71071065 0.72978729]]\n",
      "Predicted y: [[0.7920818]]\n",
      "Original label: sin\n",
      "Predict label: sin\n",
      "Correct prediction\n",
      "\n",
      "Input x: [[ 6.00015894 -0.14864427]]\n",
      "Predicted y: [[0.00051925]]\n",
      "Original label: sin\n",
      "Predict label: sin\n",
      "Correct prediction\n",
      "\n",
      "Input x: [[2.72963206 0.46373129]]\n",
      "Predicted y: [[0.00078517]]\n",
      "Original label: sin\n",
      "Predict label: sin\n",
      "Correct prediction\n",
      "\n",
      "Input x: [[ 3.17618476 -0.03072847]]\n",
      "Predicted y: [[0.0058443]]\n",
      "Original label: sin\n",
      "Predict label: sin\n",
      "Correct prediction\n",
      "\n",
      "Input x: [[1.35223708 0.23601133]]\n",
      "Predicted y: [[0.9893209]]\n",
      "Original label: cos\n",
      "Predict label: cos\n",
      "Correct prediction\n",
      "\n",
      "Input x: [[1.50318447 0.20449568]]\n",
      "Predicted y: [[0.9873704]]\n",
      "Original label: cos\n",
      "Predict label: cos\n",
      "Correct prediction\n",
      "\n",
      "Input x: [[2.50321096 0.57141644]]\n",
      "Predicted y: [[0.00090992]]\n",
      "Original label: sin\n",
      "Predict label: sin\n",
      "Correct prediction\n",
      "\n",
      "Input x: [[ 3.05668474 -0.89597362]]\n",
      "Predicted y: [[0.9993864]]\n",
      "Original label: cos\n",
      "Predict label: cos\n",
      "Correct prediction\n",
      "\n",
      "Input x: [[0.05660527 0.05237984]]\n",
      "Predicted y: [[2.2743032e-05]]\n",
      "Original label: sin\n",
      "Predict label: sin\n",
      "Correct prediction\n",
      "\n",
      "Input x: [[0.29560531 1.02991259]]\n",
      "Predicted y: [[0.99979734]]\n",
      "Original label: cos\n",
      "Predict label: cos\n",
      "Correct prediction\n",
      "\n",
      "Input x: [[ 3.93721121 -0.5295586 ]]\n",
      "Predicted y: [[0.5452576]]\n",
      "Original label: sin\n",
      "Predict label: sin\n",
      "Correct prediction\n",
      "\n",
      "Input x: [[ 5.66681678 -0.57987648]]\n",
      "Predicted y: [[2.1745367e-07]]\n",
      "Original label: sin\n",
      "Predict label: sin\n",
      "Correct prediction\n",
      "\n"
     ]
    }
   ],
   "source": [
    "\n",
    "# 假设 test_data 是包含 'x', 'y' 和 'curve' 字段的列表\n",
    "# run_model_input_x 是你要预测的输入数据\n",
    "\n",
    "test_label = {'sin': 0, 'cos': 1}\n",
    "label = [curve for curve in test_label]\n",
    "test_prediction_rate = [0] * len(test_label) * 2\n",
    "\n",
    "\n",
    "for i in range(len(run_model_input_x)):\n",
    "    # 获取输入数据\n",
    "    input_x = np.array([run_model_input_x[i]])\n",
    "    \n",
    "    # 预测输出\n",
    "    output_y = test_model.predict(input_x)\n",
    "    \n",
    "    # 打印输入数据\n",
    "    print(f\"Input x: {input_x}\")\n",
    "    \n",
    "    # 如果你有原始数据集 test_data，可以通过索引找到对应的原始标签\n",
    "    original_label = test_data[i]['curve']\n",
    "    predicted_label = label[int((output_y > 0.9).item())]\n",
    "    \n",
    "    # 打印预测结果和原始标签\n",
    "    print(f\"Predicted y: {output_y}\")\n",
    "    print(f\"Original label: {original_label}\")\n",
    "    print(f\"Predict label: {predicted_label}\")\n",
    "        \n",
    "    if int((output_y > 0.9).item()) == test_label[[_['curve'] for _ in test_data][i]]:\n",
    "        print(\"Correct prediction\\n\")\n",
    "        test_prediction_rate[test_label[[_['curve'] for _ in test_data][i]]] += 1\n",
    "    else:\n",
    "        print(\"Incorrect predicti\\n\")\n",
    "        test_prediction_rate[len(test_label) + test_label[[_['curve'] for _ in test_data][i]]] += 1\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----------------------------------------\n",
      "Accuracy of test data: \n",
      "[56, 37, 0, 7]\n",
      "\n",
      "----------------------------------------\n",
      "Label sin accuracy: \n",
      "1.0\n",
      "\n",
      "----------------------------------------\n",
      "Label cos accuracy: \n",
      "0.8409090909090909\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print('-' * 40)  \n",
    "print('Accuracy of test data: ')\n",
    "print(f'{test_prediction_rate}\\n')\n",
    "\n",
    "for i in range(len(test_label)):\n",
    "    print('-' * 40)\n",
    "    print(f'Label {label[i]} accuracy: ')\n",
    "    print(f'{test_prediction_rate[i]/run_test_label_num[i]}\\n')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input_1 (InputLayer)         [(None, 2)]               0         \n",
      "_________________________________________________________________\n",
      "relu (Dense)                 (None, 16)                48        \n",
      "_________________________________________________________________\n",
      "relu2 (Dense)                (None, 16)                272       \n",
      "_________________________________________________________________\n",
      "output (Dense)               (None, 1)                 17        \n",
      "=================================================================\n",
      "Total params: 337\n",
      "Trainable params: 337\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "import numpy as np\n",
    "import sys\n",
    "import os\n",
    "\n",
    "if not os.path.exists('./model'):\n",
    "    sys.exit(\"Model directory not found. Please run the model_training.py script first.\")\n",
    "\n",
    "# 确认文件是否存在\n",
    "model_path = './model/model.h5'\n",
    "if not os.path.exists(model_path):\n",
    "    raise FileNotFoundError(f\"Model file not found at: {model_path}\")\n",
    "\n",
    "# 加载整个模型\n",
    "try:\n",
    "    model = tf.keras.models.load_model(model_path)\n",
    "except OSError as e:\n",
    "    print(f\"Error loading model: {e}\")\n",
    "    raise\n",
    "\n",
    "# 打印模型的结构，检查层名\n",
    "model.summary()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "def read_header_file(header_file: str, model_file):\n",
    "    try:\n",
    "        if not os.path.exists(header_file):\n",
    "            print(f\"{header_file} file exists\")\n",
    "            return\n",
    "\n",
    "        with open(header_file, \"r\") as header_file:\n",
    "            for data in header_file.readlines():\n",
    "                print(data.strip(\"\\n\"))\n",
    "                model_file.write(data)\n",
    "                \n",
    "            print(\"\\n\")\n",
    "            model_file.write(\"\\n\")\n",
    "            header_file.close()\n",
    "    except Exception  as e:\n",
    "        print(e)\n",
    "\n",
    "def save_name_header(header_file: str, model_file, type: str = \"cpp\"):\n",
    "    read_header_file(header_file, model_file)\n",
    "    if type == \"cpp\":\n",
    "        model_file.write('#include \"weight.h\"\\n\\n')\n",
    "    if type == \"h\":\n",
    "        model_file.write(\"#ifndef WEIGHT_H\\n\")\n",
    "        model_file.write(\"#define WEIGHT_H\\n\\n\")\n",
    "\n",
    "def save_model_weight(model_file, layer, W, b):\n",
    "    if layer is None: return\n",
    "    \n",
    "    if W is None: return\n",
    "    model_file.write(f\"const float W_{layer.name}[] = \" + \"{\\n\")\n",
    "    model_file.write(\"   \" + str(\", \".join([str(x) + \"f\" for x in W.flatten()])) + \"\\n\")\n",
    "    model_file.write(\"};\\n\\n\")\n",
    "\n",
    "    if b is None: return\n",
    "    model_file.write(f\"const float b_{layer.name}[] = \" + \"{\\n\")\n",
    "    model_file.write(\"   \" + str(\", \".join([str(x) + \"f\" for x in b.flatten()])) + \"\\n\")\n",
    "    model_file.write(\"};\\n\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "// Copyright (c) 2025. BNU-HKBU UIC RoboMaster\n",
      "//\n",
      "// This program is free software: you can redistribute it\n",
      "// and/or modify it under the terms of the GNU General\n",
      "// Public License as published by the Free Software\n",
      "// Foundation, either version 3 of the License, or (at\n",
      "// your option) any later version.\n",
      "//\n",
      "// This program is distributed in the hope that it will be\n",
      "// useful, but WITHOUT ANY WARRANTY; without even\n",
      "// the implied warranty of MERCHANTABILITY or FITNESS\n",
      "// FOR A PARTICULAR PURPOSE.  See the GNU General\n",
      "// Public License for more details.\n",
      "//\n",
      "// You should have received a copy of the GNU General\n",
      "// Public License along with this program.  If not, see\n",
      "// <https://www.gnu.org/licenses/>.\n",
      "\n",
      "\n",
      "  Weight (2, 16)\n",
      "  Bias  (16,)\n",
      "  Weight (16, 16)\n",
      "  Bias  (16,)\n",
      "  Weight (16, 1)\n",
      "  Bias  (1,)\n",
      "Weights have been saved to model.cpp\n"
     ]
    }
   ],
   "source": [
    "file_type =  \"cpp\"\n",
    "\n",
    "# 创建或打开一个文件来保存权重数据\n",
    "with open('./model/weight.cpp', 'w') as f:\n",
    "\n",
    "    save_name_header(\"header.txt\", f, str(file_type))\n",
    "\n",
    "    for layer in model.layers:\n",
    "        weights = layer.get_weights()\n",
    "        if weights:\n",
    "            # 解包权重和偏置\n",
    "            if len(weights) == 2: \n",
    "                W, b = weights\n",
    "                print(f\"  Weight {W.shape}\")\n",
    "                print(f\"  Bias  {b.shape}\")\n",
    "                save_model_weight(f, layer, W, b)\n",
    "\n",
    "            else:\n",
    "                print(f\"  Weight : {weight.shape}\")\n",
    "                save_model_weight(f, layer, W, None)\n",
    "\n",
    "    if str(file_type) == \"h\":\n",
    "        f.write(\"#endif\")\n",
    "\n",
    "    f.close()\n",
    "\n",
    "\n",
    "print(\"Weights have been saved to model.cpp\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Next content is use to test weight.\n",
    "##### by trained save to weight and make to lib file, next to deploy to stm32f1 mcu, so you can achieve Cotex-M3 with Tensorflow\n",
    "##### but my is use stm32f1, so RAM is 72K, and ROM is 128K, do worry about the size of RAM\n",
    "##### if you want to the ues to ArduinoUNO, that you be take care about the size of RAM and ROM, becone ArduinoUNO RAM is 2K, ROM is 32K"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "W_relu = [\n",
    "   -0.24149461, 0.47922814, 0.50131214, -0.4798298, -0.2532205, 0.3741976, 0.5106996, -0.5723628, 0.29312927, 0.14284667, 0.18007861, 0.53011316, 0.27659953, 0.513101, -0.35395074, 0.54553413, -0.16416028, -0.6303126, -0.4120823, 0.2846507, 0.23997353, 0.33834803, -0.31282267, -0.0821689, 0.64450747, 0.21232021, -0.21048163, 0.14289859, 0.9056437, -0.02009336, 0.5585036, 0.11996085\n",
    "]\n",
    "\n",
    "b_relu = [\n",
    "   0.2994836, -1.6755055, 0.14872806, 0.18468706, 1.1543086, -0.43254453, -0.13702036, -0.016588408, -0.41021687, 0.36008996, 0.7056856, -0.47748086, -0.37318128, 0.09698134, 0.12443947, -0.033093266\n",
    "]\n",
    "\n",
    "W_relu2 = [\n",
    "   -0.11274072, -0.26884854, -0.31818074, 0.6240941, 0.34621048, -2.4826257, -0.33750778, -0.12793626, 0.32733333, -0.48746654, -1.6765659, 0.09442402, -1.6495746, -0.37134352, -0.06523157, 0.196599, 0.2872597, -0.94669527, -0.4405022, 0.39935574, -0.18897972, 0.0573984, 0.13863692, -0.2820638, -0.42445695, -0.17804939, -0.34729517, -0.47730774, -0.2136918, -0.2470234, 0.40257385, -0.91330934, 0.38943264, -0.40674022, 0.061517186, -0.32632932, 0.15824439, 0.24751, 0.23129983, 0.3426468, 0.2379264, 0.23518258, -1.3227779, 0.28530386, -1.0199721, -0.3314736, 0.32155734, -0.009451947, -0.24686457, -0.44752792, 0.44205517, -0.4762933, -0.0024825886, 0.49246085, 0.07856348, -0.57931805, 0.29061812, 0.35466126, 0.41234294, 0.3665784, -0.07089147, 0.13843477, -0.34019142, -0.16569981, 0.5123804, 0.46304482, 0.15424874, 0.5077822, 0.011534924, -0.70566744, -0.029166967, 0.00042905967, 0.037140716, 0.060699154, 0.15924428, -0.4728702, 0.3066106, 0.04420945, -5.6324525, 0.08400713, -0.04175812, -0.3746421, 0.059250303, 0.12598291, 0.14477508, 0.22857308, 0.25278407, -1.7651987, -0.4251639, -0.25146306, -0.43776813, 0.089096405, -0.35222173, 0.10650184, -0.10793213, -0.18450674, 0.3170716, 0.4021158, -0.19625016, 0.19961153, 0.14310044, 0.32879505, -0.16241178, -0.40421972, -0.06590737, -0.03332301, -0.72458214, 0.26022598, -0.63882923, -0.06313098, 0.38335463, 0.38139522, -0.052731417, 0.0479196, 0.089213274, 0.045557708, -0.40378654, 0.3963642, -0.33036295, -0.024431698, 0.20503017, -0.44227433, 0.17165463, 0.12996592, -0.06749952, 0.14674279, 0.2079375, 0.20110855, 0.7082237, 0.3215022, -0.45005456, 0.66308486, -0.018726353, -0.48203918, -0.09722185, -1.1290166, 0.15686853, -0.63658667, 0.15544558, -0.5386509, 0.20366716, -0.070439056, 0.568773, 0.20577393, -0.28900704, 0.21761343, 0.1713223, -0.01243686, 0.2705438, -0.008152074, -0.26546136, 0.30531147, 0.4735157, 0.2792148, -0.10548058, 0.4089599, -0.09441017, -0.2400988, -0.041295737, -0.29637372, 0.052823853, 0.46683854, 0.018222768, -0.4375097, 0.093791105, 0.31804574, 0.16785061, 0.30490515, 0.037038468, 0.1784709, -0.21872039, 0.27974132, -0.10488409, -0.39632067, -0.4423851, 0.29102498, -0.16430871, -0.14018042, -0.46373674, 0.3474527, 0.21232437, -0.41460693, -0.41148677, -1.1273178, -0.25038847, -0.030042809, -0.58375645, -0.096017785, -1.0291692, -0.047899865, 0.20118959, -0.13664098, -0.29437074, 0.25477996, 0.63920593, -0.24880551, 0.19279757, 0.4970879, -0.28775406, 0.20165068, 0.40909508, 0.14107655, 0.40997827, 0.48870438, 0.19164604, -0.19181383, 0.93361604, 0.3333709, 0.19139722, -0.23319405, 0.30243117, 0.44087267, -0.07126297, 0.23621795, -0.18401504, -0.3844879, 0.105194, 0.28428102, -0.38414872, -0.021287136, 0.014290982, 0.31905177, -0.14982346, -0.43699184, 0.5514378, 0.43114385, -0.1313246, 0.43331885, 0.44505337, 0.26983947, -0.41944802, -0.3832314, -0.35993043, 0.007841324, 0.4314582, -0.28144726, 0.49208882, 0.26263615, -0.6908846, 0.64596707, -0.0043521724, 0.18205985, 0.049872175, 0.4354612, 0.1696114, 0.23274001, -0.2093554, 0.038454823, -0.21336825, 0.191473, 0.31857556, 0.101667956, -0.12728742, 0.07645066, -0.29942256, 0.1961503\n",
    "]\n",
    "\n",
    "b_relu2 = [\n",
    "   -0.34272587, 0.2451981, 0.5447643, -0.30491856, -0.28598183, 0.24251898, -0.020264165, 0.16817524, 0.4345641, 0.30907702, 0.19763012, 0.31916845, -0.02726964, -0.03379958, 0.012561608, 0.28955373\n",
    "]\n",
    "\n",
    "W_output = [\n",
    "   -0.51435775, -0.84773326, 1.0575833, -0.7169872, -0.5358879, 0.1627581, 0.5270894, -2.1309624, 0.9061274, 0.5968292, 0.6464468, 0.2340433, 0.98980683, -0.17969719, 1.0373071, -0.9638522\n",
    "]\n",
    "\n",
    "b_output = [\n",
    "   0.31032756\n",
    "]\n",
    "\n",
    "DENSE1_SIZE = 16\n",
    "DENSE2_SIZE = 16"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "notebookRunGroups": {
     "groupValue": "1"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "array([1.46544762, 0.16031256])\n",
      "relu1: [0.0, 0.0, 0.8173127739964188, 0.0, 0.8216979918663267, 0.17006389118996884, 0.5612337503012168, 0.0, 0.12267136352866054, 0.603461868981263, 0.9358385214991354, 0.3222806474359695, 0.17734690292649052, 0.8456827612890184, 0.0, 0.7855896574005465]\n",
      "----------------------------------------\n",
      "relu2: 0.5826257681860512\n",
      "relu2: 1.0086841432669007\n",
      "relu2: 0.9458020297420346\n",
      "relu2: 0.426248352446745\n",
      "relu2: 0.38210355800758744\n",
      "relu2: 0.6590978411669259\n",
      "relu2: 0\n",
      "relu2: 0\n",
      "relu2: 0.803099590186833\n",
      "relu2: 1.1532968486160906\n",
      "relu2: 0\n",
      "relu2: 0.8850941631307532\n",
      "relu2: 0\n",
      "relu2: 0\n",
      "relu2: 0\n",
      "relu2: 0.451898409303912\n",
      "----------------------------------------\n",
      "Output: [0.9403319]\n"
     ]
    }
   ],
   "source": [
    "\n",
    "def relu(x):\n",
    "    return max(0, x)\n",
    "\n",
    "def predict(x1: float, x2: float):\n",
    "    w1 = np.zeros(DENSE1_SIZE, dtype=float)\n",
    "\n",
    "    for i in range(DENSE1_SIZE):\n",
    "        w1[i] = relu(x1 * W_relu[i] + x2 * W_relu[16 + i] + b_relu[i])\n",
    "    print(f\"relu1: {[_ for _ in w1]}\")\n",
    "    print(\"-\" * 40)\n",
    "\n",
    "    w3 = 0.0\n",
    "    for relu_x2 in range(DENSE2_SIZE):\n",
    "        w2 = 0.0\n",
    "        for relu_y2 in range(DENSE1_SIZE):\n",
    "            w2 += w1[relu_y2] * W_relu2[relu_y2 * DENSE1_SIZE + relu_x2]\n",
    "\n",
    "        w2 = relu(w2 + b_relu2[relu_x2])\n",
    "        print(f\"relu2: {w2}\")\n",
    "\n",
    "        w3 += w2 * W_output[relu_x2]\n",
    "\n",
    "    print(\"-\" * 40)\n",
    "    print(f\"Output: {w3 + b_output}\")\n",
    "\n",
    "# Input x: [[1.46544762 0.16031256]]\n",
    "# Predicted y: [[0.9403317]]\n",
    "# Original label: cos\n",
    "# Predict label: cos\n",
    "# Correct prediction\n",
    "input_x = np.array([1.46544762, 0.16031256])\n",
    "print(input_x)\n",
    "predict(1.46544762, 0.16031256)"
   ]
  }
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